R Notebook
NodeNumber analysis
library(plyr)
library(dplyr)
Attaching package: <U+393C><U+3E31>dplyr<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:plyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarize
The following objects are masked from <U+393C><U+3E31>package:stats<U+393C><U+3E32>:
filter, lag
The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(lubridate)
Attaching package: <U+393C><U+3E31>lubridate<U+393C><U+3E32>
The following object is masked from <U+393C><U+3E31>package:plyr<U+393C><U+3E32>:
here
The following object is masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:
datelibrary(hydroGOF)Loading required package: zoo
Attaching package: <U+393C><U+3E31>zoo<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:
as.Date, as.Date.numericlibrary(xtable)
library(knitr)
library(tidyr)
library(RSQLite)
library(agricolae)
library(scales)
library(zoo)
detach(package:plyr)### load obs data
upDir <- "D:/R/CombinedData/"
obsData <- "D:/R/CombinedData/"
obsAll <- read.table(paste0(obsData, "ObsAll.txt"),
header = TRUE)
obsA1<- obsAll %>%
dplyr::filter(Collection=="2000_2002")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(ifelse(Rotation=="1",paste0("S",Rotation),paste0("R",Rotation))))
obsA4<- obsAll %>%
dplyr::filter(Collection=="2014_2018")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(ifelse(Rotation=="1",paste0("S",Rotation),paste0("R",Rotation))))
obsA2<- obsAll %>%
dplyr::filter(Collection=="1997_2001")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(paste0("R",Rotation)))
obsA3<- obsAll %>%
dplyr::filter(Collection=="2002_2004")%>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))%>%
mutate(GrowthSeason2=as.factor(paste0("Y",GrowthSeason,"(",Period,")"))) %>%
mutate(Rotation2=as.factor(paste0("R",Rotation)))
obsA<-rbind(obsA2,obsA3,obsA1,obsA4)
summary(obsA) Clock.Today Name Collection Experiment Water
Min. :1997-01-28 Iversen_91DefoliationLL :895 1997_2001: 955 Lincoln2000:1218 dry: 973
1st Qu.:2001-02-10 Iversen_121DefoliationLLFDFD5 :777 2000_2002:1067 Lincoln2015:1132 irr:3078
Median :2002-08-22 Iversen_8Waterirr :494 2002_2004: 895 Lincoln2001: 387
Mean :2005-09-21 Iversen_8Waterdry :461 2010_2012: 0 Lincoln2003: 384
3rd Qu.:2015-03-09 Iversen_121DefoliationHHFDFD5 :288 2014_2018:1134 Lincoln2002: 257
Max. :2018-01-15 Iversen_9SowingDateSD1Waterirr:277 Lincoln2004: 254
(Other) :859 (Other) : 419
Defoliation SowingDate FD Period GrowthSeason Rotation StartDate
HH: 288 No :1850 FD10: 35 00_01 :995 Min. :1.000 Min. :1.000 Min. :1996-11-01
LL:3763 no :1134 FD2 : 34 01_02 :522 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2000-12-27
SD1 : 536 FD5 :3982 02_03 :432 Median :2.000 Median :3.000 Median :2002-06-14
SD2 : 199 03_04 :412 Mean :2.277 Mean :2.983 Mean :2005-08-15
SD3 : 169 15_16 :410 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2015-01-30
SD4 : 163 16_17 :342 Max. :6.000 Max. :7.000 Max. :2017-12-04
(Other): 0 (Other):938
MidDate FinishDate Interval Variable VariableUnits Time
Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00 NodeNumber :792 % : 220 12:00:00:4051
1st Qu.:2001-02-10 1st Qu.:2001-03-06 1st Qu.: 19.00 Height :697 cm : 454
Median :2002-08-09 Median :2002-10-05 Median : 32.00 shootbiomass:585 fractio0l: 833
Mean :2005-09-14 Mean :2005-10-15 Mean : 36.97 LAI :538 Fraction : 434
3rd Qu.:2015-03-13 3rd Qu.:2015-04-24 3rd Qu.: 48.00 SWC :284 kg/ha :1045
Max. :2017-12-25 Max. :2018-01-15 Max. :122.00 Fraction :220 m2/m2 : 538
(Other) :935 mm : 527
Observed StdDEV ExperimentID TreatmentID ID GrowthSeason1 Rotation1
Min. : 0.00 Min. : 0.0000 E1: 955 ILL :1389 E3ILL :895 Gs_1:1352 Rt_1:1175
1st Qu.: 3.50 1st Qu.: 0.0000 E2:1067 ILLF5 : 777 E5ILLF5:777 Gs_2:1447 Rt_2: 823
Median : 12.47 Median : 0.2582 E3: 895 DLL : 461 E1ILL :494 Gs_3: 530 Rt_3: 613
Mean : 585.40 Mean : 88.3883 E4: 0 IHHF5 : 288 E1DLL :461 Gs_4: 272 Rt_4: 458
3rd Qu.: 623.15 3rd Qu.: 30.3450 E5:1134 ILLS1 : 277 E5IHHF5:288 Gs_5: 399 Rt_5: 443
Max. :8556.38 Max. :2532.5264 DLLS1 : 259 E2ILLS1:277 Gs_6: 51 Rt_6: 397
NA's :1192 (Other): 600 (Other):859 Rt_7: 142
ExpUnitCode Clock.Today1 GrowthSeason2 Rotation2
Iversen_91DefoliationLLGs_2Rt_1: 84 Min. :1997-01-28 12:00:00 Y1(00_01): 596 R2 :823
Iversen_91DefoliationLLGs_1Rt_1: 72 1st Qu.:2001-02-10 12:00:00 Y1(02_03): 432 S1 :768
Iversen_91DefoliationLLGs_1Rt_4: 67 Median :2002-08-22 12:00:00 Y2(01_02): 428 R3 :613
Iversen_91DefoliationLLGs_1Rt_5: 67 Mean :2005-09-21 17:05:34 Y2(03_04): 412 R4 :458
Iversen_91DefoliationLLGs_1Rt_3: 66 3rd Qu.:2015-03-09 12:00:00 Y2(15_16): 410 R5 :443
Iversen_91DefoliationLLGs_1Rt_6: 62 Max. :2018-01-15 12:00:00 Y5(00_01): 399 R1 :407
(Other) :3633 (Other) :1374 (Other):539 obsAupDir <- "D:/R/"
obsData <- "D:/R/TtAll/"
Tt<- read.table(paste0(obsData, "df.all.txt"),
header = TRUE)
TtA <- Tt %>% mutate(Clock.Today=dmy(Clock.Today), ExpUnitCode=as.factor(ExpName))
TtAObsH <-merge(obsA,TtA,by=c("Clock.Today","ExpUnitCode")) %>%
mutate(GrowthRotation=as.factor(paste0(GrowthSeason.x,Rotation.x)))%>%
dplyr::filter(Water.x=="irr")%>%
dplyr::filter(Defoliation.x=="LL")%>%
dplyr::filter(Variable=="NodeNumber")
summary(ObsH) Clock.Today ExpUnitCode Name
Min. :1997-10-23 Iversen_121DefoliationLLFDFD5Gs_1Rt_1 : 85 Iversen_121DefoliationLLFDFD5 :605
1st Qu.:2000-12-23 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 60 Iversen_8Waterirr :605
Median :2002-01-08 Iversen_8WaterirrGs_4Rt_5 : 55 Iversen_91DefoliationLL :455
Mean :2005-06-12 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 55 Iversen_9SowingDateSD1Waterirr:300
3rd Qu.:2014-11-30 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 55 Iversen_9SowingDateSD2Waterirr:110
Max. :2018-01-15 Iversen_8WaterirrGs_5Rt_1 : 50 Iversen_9SowingDateSD3Waterirr:100
(Other) :1895 (Other) : 80
Collection Experiment.x Water.x Defoliation.x SowingDate.x FD.x Period
1997_2001:605 Lincoln2000:755 dry: 0 HH: 0 No :1060 FD10: 0 00_01 :630
2000_2002:590 Lincoln2015:605 irr:2255 LL:2255 no : 605 FD2 : 0 99_00 :230
2002_2004:455 Lincoln2003:225 SD1 : 300 FD5 :2255 02_03 :215
2010_2012: 0 Lincoln2001:185 SD2 : 110 03_04 :205
2014_2018:605 Lincoln1999:140 SD3 : 100 16_17 :195
Lincoln2004:140 SD4 : 80 15_16 :175
(Other) :205 (Other): 0 (Other):605
GrowthSeason.x Rotation.x StartDate MidDate FinishDate
Min. :1.000 Min. :1.000 Min. :1997-10-07 Min. :1997-10-28 Min. :1997-11-19
1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2000-11-14 1st Qu.:2000-12-29 1st Qu.:2001-02-13
Median :2.000 Median :3.000 Median :2001-12-22 Median :2002-01-16 Median :2002-02-11
Mean :2.317 Mean :3.118 Mean :2005-05-10 Mean :2005-06-08 Mean :2005-07-08
3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:2014-11-03 3rd Qu.:2014-12-16 3rd Qu.:2015-01-29
Max. :5.000 Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15
Interval Variable VariableUnits Time Observed StdDEV
Min. : 0.00 NodeNumber:2255 % : 0 12:00:00:2255 Min. : 0.000 Min. :0.0000
1st Qu.: 17.00 Branch : 0 cm : 0 1st Qu.: 5.100 1st Qu.:0.0000
Median : 29.00 CrownWt : 0 fractio0l:1650 Median : 7.800 Median :0.2600
Mean : 32.86 Fraction : 0 Fraction : 605 Mean : 7.668 Mean :0.3549
3rd Qu.: 40.00 HardStemWt: 0 kg/ha : 0 3rd Qu.:10.400 3rd Qu.:0.5774
Max. :116.00 Height : 0 m2/m2 : 0 Max. :16.667 Max. :2.1000
(Other) : 0 mm : 0 NA's :520
ExperimentID TreatmentID ID GrowthSeason1 Rotation1 Clock.Today1
E1:605 ILL :1060 E1ILL :605 Gs_1:805 Rt_1:615 Min. :1997-10-23 12:00:00
E2:590 ILLF5 : 605 E5ILLF5:605 Gs_2:645 Rt_2:430 1st Qu.:2000-12-23 12:00:00
E3:455 ILLS1 : 300 E3ILL :455 Gs_3:300 Rt_3:325 Median :2002-01-08 12:00:00
E4: 0 ILLS2 : 110 E2ILLS1:300 Gs_4:295 Rt_4:275 Mean :2005-06-12 20:46:57
E5:605 ILLS3 : 100 E2ILLS2:110 Gs_5:210 Rt_5:265 3rd Qu.:2014-11-30 12:00:00
ILLS4 : 80 E2ILLS3:100 Gs_6: 0 Rt_6:260 Max. :2018-01-15 12:00:00
(Other): 0 (Other): 80 Rt_7: 85
GrowthSeason2 Rotation2 year day rain maxt mint
Y1(00_01):420 R2 :430 Min. :1997 Min. : 1.0 Min. : 0.0000 Min. : 7.90 Min. :-4.90
Y4(99_00):230 S1 :420 1st Qu.:2000 1st Qu.: 63.0 1st Qu.: 0.0000 1st Qu.:15.10 1st Qu.: 4.70
Y1(02_03):215 R3 :325 Median :2002 Median :138.0 Median : 0.0000 Median :18.80 Median : 8.60
Y5(00_01):210 R4 :275 Mean :2005 Mean :171.9 Mean : 0.9233 Mean :19.02 Mean : 7.97
Y2(03_04):205 R5 :265 3rd Qu.:2014 3rd Qu.:297.0 3rd Qu.: 0.0000 3rd Qu.:22.00 3rd Qu.:11.30
Y3(16_17):195 R6 :260 Max. :2018 Max. :365.0 Max. :35.3000 Max. :33.80 Max. :20.60
(Other) :780 (Other):280
mean radn wind vp Pp Tb TTbeta
Min. : 2.55 Min. : 1.30 Min. :0.600 Min. : 5.10 Min. :10.02 Min. :1 Min. : 0.03021
1st Qu.:10.50 1st Qu.: 9.70 1st Qu.:2.800 1st Qu.:10.08 1st Qu.:12.15 1st Qu.:2 1st Qu.: 2.12367
Median :13.20 Median :16.40 Median :3.900 Median :11.60 Median :14.28 Median :3 Median : 3.87643
Mean :13.49 Mean :16.89 Mean :3.923 Mean :11.91 Mean :13.96 Mean :3 Mean : 5.43220
3rd Qu.:16.50 3rd Qu.:23.10 3rd Qu.:4.800 3rd Qu.:14.00 3rd Qu.:15.92 3rd Qu.:4 3rd Qu.: 7.68109
Max. :26.20 Max. :34.50 Max. :9.300 Max. :22.90 Max. :16.65 Max. :5 Max. :23.34334
Tbb TTbroken TbF TTfick ExpName
Min. :1 Min. : 0.6683 Min. :1 Min. : 0.6683 Iversen_121DefoliationLLFDFD5Gs_1Rt_1 : 85
1st Qu.:2 1st Qu.: 6.3519 1st Qu.:2 1st Qu.: 6.9410 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 60
Median :3 Median : 8.7409 Median :3 Median : 9.6773 Iversen_8WaterirrGs_4Rt_5 : 55
Mean :3 Mean : 9.1513 Mean :3 Mean : 9.8401 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 55
3rd Qu.:4 3rd Qu.:11.7322 3rd Qu.:4 3rd Qu.:12.7247 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 55
Max. :5 Max. :20.2576 Max. :5 Max. :20.8129 Iversen_8WaterirrGs_5Rt_1 : 50
(Other) :1895
Experiment.y Water.y Defoliation.y SowingDate.y FD.y GrowthSeason.y Rotation.y
Lincoln1997:605 dry: 0 HH: 0 No :1060 FD10: 0 Gs_1:805 Rt_1 :615
Lincoln2000:510 irr:2255 LL:2255 no : 605 FD2 : 0 Gs_2:645 Rt_2 :430
Lincoln2001: 80 LS: 0 SD1 : 300 FD5 :2255 Gs_3:300 Rt_3 :325
Lincoln2002:455 SL: 0 SD2 : 110 Gs_4:295 Rt_4 :275
Lincoln2010: 0 SS: 0 SD3 : 100 Gs_5:210 Rt_5 :265
Lincoln2015:605 SD4 : 80 Gs_6: 0 Rt_6 :260
(Other): 0 (Other): 85
Tt_beta_sum Tt_fick_sum Tt_broken_sum Ppm Tmean GrowthRotation
Min. : 0.0967 Min. : 1.36 Min. : 1.36 Min. :10.25 Min. : 7.024 11 : 330
1st Qu.: 63.1925 1st Qu.: 162.47 1st Qu.: 151.24 1st Qu.:11.99 1st Qu.:11.337 12 : 190
Median :122.2336 Median : 274.04 Median : 254.09 Median :14.52 Median :13.963 13 : 115
Mean :152.1671 Mean : 298.79 Mean : 276.59 Mean :13.89 Mean :13.282 21 : 115
3rd Qu.:211.0533 3rd Qu.: 401.50 3rd Qu.: 372.81 3rd Qu.:15.99 3rd Qu.:15.771 26 : 115
Max. :722.6708 Max. :1186.65 Max. :1051.88 Max. :16.55 Max. :19.327 22 : 110
(Other):1280 mytheme1<-theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14,angle=90, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
mytheme3<-theme(
legend.title = element_text(colour="black", size=14, face="bold"),
legend.text = element_text(colour="black", size = 14,face="plain"),
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14),
legend.position = "right", legend.box = "vertical")Examples for order text
x1 <- c("white","white","blue","green","red","blue","red")
ord <- c("red","white","blue","green")
f1 <- factor(x1,levels=ord)
sort(f1)[1] red red white white blue blue green
Levels: red white blue greenx2 <- as.character(sort(f1))
x <- c("white","white","blue","green","red","blue","red")
y <- c("red","white","blue","green")
x[order(match(x, y))][1] "red" "red" "white" "white" "blue" "blue" "green"obsNode<-ObsH%>%
dplyr::filter(Tb==1)
obsNode0<-obsNode%>%
dplyr::filter(Name=="Iversen_8Waterirr")
obsNode0$Rotation2<- factor(obsNode0$Rotation2, levels=c("R1", "R2", "R3", "R4", "R5","R6", "R7"))
obsNode0%>%
ggplot(aes(x=Tt_broken_sum, y=Observed))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd) ")+ylab("Node number")+ggtitle(paste0("E1ILL","(Iversen_8Waterirr)")) +
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+mytheme1
#to save a final graph
#ggsave("D:/R/Pictures/Node/Iversen_8Waterirrnode.png", width=8, height=6, dpi=500)#fig.height=4, fig.width=8
obsNode1<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD1Waterirr")
obsNode1$Rotation2<- factor(obsNode1$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode1%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd) ")+scale_x_continuous(limits=c(0,425))+
ylab("Node number")+ggtitle(paste0("E2ILLS1","(Iversen_9SowingDateSD1Waterirr)"))+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+
theme(axis.title.y=element_text(face="bold",colour="black",size = 14))+mytheme1Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD1Waterirrnode.png", width=8, height=4, dpi=500)
obsNode2<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD2Waterirr")
obsNode2$Rotation2<- factor(obsNode2$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode2%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+ggtitle(paste0("E2ILLS2","(Iversen_9SowingDateSD2Waterirr)"))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD2Waterirrnode.png", width=8, height=3, dpi=500)
obsNode1<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD3Waterirr")
obsNode1$Rotation2<- factor(obsNode1$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode1%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+ggtitle("E2ILLS3(Iversen_9SowingDateSD3Waterirr)")+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 14))+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD3Waterirrnode.png", width=8, height=3, dpi=500)
obsNode1<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Name=="Iversen_9SowingDateSD4Waterirr")
obsNode1$Rotation2<- factor(obsNode1$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6"))
obsNode1%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+ggtitle("E2ILLS4(Iversen_9SowingDateSD4Waterirr)")+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDateSD4Waterirrnode.png", width=8, height=3, dpi=500)
obsNode4<-obsNode%>%
#dplyr::filter(Ture!="No")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
obsNode4$Rotation2<- factor(obsNode4$Rotation2, levels=c("R1", "R2", "R3", "R4", "R5","R6","R7"))
obsNode4%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+ylab("Node number")+
#ggtitle("E3ILL(Iversen_91DefoliationLL)")+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
facet_grid(GrowthSeason2~Rotation2)+
mytheme1
#ggsave("D:/R/Pictures/Node/Iversen_91DefoliationLLnode.png", width=8, height=5, dpi=500)
ggsave("D:/R/Pictures/Poster/nodenumberE3.png", width=8, height=4, dpi=500)obsNodeLL<-obsNode%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")
obsNodeLL$Rotation2<- factor(obsNodeLL$Rotation2, levels=c("S1", "R2", "R3", "R4", "R5","R6","R7"))
obsNodeLL%>%
ggplot(aes(x=Tt_beta_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time (°Cd)")+scale_x_continuous(limits=c(0,430))+
ylab("Node number")+ggtitle("E5ILLF5(Iverson_121DefoliationLLFDFD5)")+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="blue")+
scale_x_continuous(breaks = seq(0, 550, by =200), limits=c(0,450))+
scale_y_continuous(breaks = seq(0, 18, by =6), limits=c(0,16))+
facet_grid(GrowthSeason2~Rotation2)+
mytheme1Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
# ggsave("D:/R/Pictures/Node/Iversen_121DefoliationLLFDFD5node.png", width=8, height=6, dpi=500)R squared and phyllo calculation for each rotation
TTWES1
Tb evaluation
#obsef1%>%
# dplyr::filter(Ture!="No")
# statWE1<-ObsH%>%
# filter(Variable == "NodeNumber") %>%
# filter(Water=="irr")%>%
# filter(Defoliation=="LL")%>%
# mutate(GrowthSeason=factor(GrowthSeason))%>%
# group_by(GrowthSeason,Collection) %>%
uniTb <- unique(ObsH$Tb)
PhyBeta.all <- data.frame()
for(i in 1:length(uniTb)){
ObsH1 <- ObsH %>%
filter(Tb==uniTb[i])
ObsH.sub <- ObsH1 %>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,Tb,ID,ExperimentID) %>%
do(mod.beta = lm(Tt_beta_sum~Observed,data=.))%>%
mutate(R2WE = summary(mod.beta)$r.squared)%>%
mutate(PhylloWE = summary(mod.beta)$coeff[2])%>%
dplyr::select(-mod.beta)
PhyBeta.all <- rbind(PhyBeta.all, ObsH.sub)
}
PhyBeta.allwrite.csv(PhyBeta.all,"D:/R/PhyllochronData/PhyBeta.all.csv", row.names = FALSE)uniTb <- unique(ObsH$Tb)
Phybroken.all <- data.frame()
for(i in 1:length(uniTb)){
ObsH1 <- ObsH %>%
filter(Tb==uniTb[i])
ObsH.sub <- ObsH1 %>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,Tb,ID,ExperimentID) %>%
do(mod.broken = lm(Tt_broken_sum~Observed,data=.))%>%
#do(mod.fick = lm(Tt_fick_sum~Observed,data=.))%>%
mutate(R2Br = summary(mod.broken)$r.squared)%>%
mutate(PhylloBr = summary(mod.broken)$coeff[2])%>%
mutate(P=anova(mod.broken)$'Pr(>F)'[1])%>%
mutate(intcp= summary(mod.broken)$coeff[1])%>%
dplyr::select(-mod.broken)
Phybroken.all <- rbind(Phybroken.all, ObsH.sub)
}ANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliable
Phybroken.allwrite.csv(Phybroken.all,"D:/R/PhyllochronData/Phybroken.all.csv", row.names = FALSE)uniTb <- unique(ObsH$Tb)
Phyfick.all <- data.frame()
for(i in 1:length(uniTb)){
ObsH1 <- ObsH %>%
filter(Tb==uniTb[i])
ObsH.sub <- ObsH1 %>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,Tb,ID,ExperimentID) %>%
do(mod.fick = lm(Tt_fick_sum~Observed,data=.))%>%
mutate(R2fi = summary(mod.fick)$r.squared)%>%
mutate(Phyllofick = summary(mod.fick)$coeff[2])%>%
dplyr::select(-mod.fick)
Phyfick.all <- rbind(Phyfick.all, ObsH.sub)
}
Phyfick.allwrite.csv(Phyfick.all,"D:/R/PhyllochronData/Phyfick.all.csv", row.names = FALSE)Combine the Phyllo data together
multmerge = function(mypath){
filenames=list.files(path=mypath, full.names=TRUE)
datalist = lapply(filenames, function(x){read.csv(file=x,header=T)})
Reduce(function(x,y) {merge(x,y)}, datalist)}
mergephy=multmerge("D:/R/PhyllochronData")
mergephymergeP<-mergephy%>%
dplyr::select(Name,GrowthSeason.x,Rotation.x,Collection,Tmean, Ppm,Tb, PhylloWE,PhylloBr,Phyllofick,ID,ExperimentID) %>%
tidyr::gather("Variable","Phyllo",PhylloWE:Phyllofick)%>%
mutate(PhylloTb=as.factor(paste0(Variable,Tb)))%>%
mutate(GrowthRotation=as.factor(paste0(GrowthSeason.x,Rotation.x)))
unique(mergeP)mergeP$Names[mergeP$Name=="Iversen_8Waterirr" ] <-"I8irr"
mergeP$Names[mergeP$Name=="Iversen_91DefoliationLL"] <-"I9LL"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD1Waterirr"]<-"I9SD1irr"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD2Waterirr"]<-"I9SD2irr"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD3Waterirr"]<-"I9SD3irr"
mergeP$Names[mergeP$Name=="Iversen_9SowingDateSD4Waterirr"]<-"I9SD4irr"
mergeP$Names[mergeP$Name=="Iversen_121DefoliationLLFDFD5"]<-"I12DF5"
mergePRegression coefficient
mergeP%>%
ggplot(aes(x=Tmean, y=Phyllo, colour=factor(Names)))+geom_point(size=2)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+xlab("Mean air temperature (°C)")+ylab("Phyllochron (°Cd/Leaf)")+
facet_wrap(~PhylloTb,ncol = 2)+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAuniPhy <- unique(mergeP$PhylloTb)
Slope.df<-data.frame()
for(i in 1:length(uniPhy) ){
mergeP.sub <- mergeP %>%
filter(PhylloTb==uniPhy[i])%>%
group_by(PhylloTb) %>%
do(modPhy = lm(Tmean~Phyllo,data=.))%>%
mutate(R2 = summary(modPhy)$r.squared)%>%
mutate(slope = summary(modPhy)$coeff[2])%>%
mutate(P=anova(modPhy)$'Pr(>F)'[1])%>%
dplyr::select(-modPhy)
Slope.df <- rbind(Slope.df,mergeP.sub)
}
Slope.dfCV coeffecient of variation
mergePmergePcv<-mergeP%>%group_by(Collection, Variable)%>%
mutate(meanPhyllo=mean(Phyllo))%>%
mutate(SDPhyllo=sd(Phyllo))%>%
mutate(CVPhyllo=SDPhyllo/meanPhyllo)
mergePcvmergePcv1<-mergePcv%>%group_by(Variable)%>%
mutate(CVmean=mean(CVPhyllo))
mergePcv%>%
ggplot(aes(x=PhylloTb,y=CVPhyllo,color=factor(Collection)))+geom_point(size=2)+theme_bw()
mergePcv1%>%
ggplot(aes(x=PhylloTb,y=CVmean))+geom_point(size=2)+theme_bw()
mergePcv1x-intercept method
LAR2<-ObsH%>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,GrowthRotation,ExperimentID,ID) %>%
do(mod = lm(Observed~Interval,data=.)) %>%
mutate(slope = summary(mod)$coeff[2]) %>%
mutate(R2 = summary(mod)$r.squared)%>%
mutate(P=anova(mod)$'Pr(>F)'[1])%>%
mutate(intcp= summary(mod)$coeff[1])%>%
dplyr::select(-mod)essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableANOVA F-tests on an essentially perfect fit are unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableLAR.ana<- ObsH%>%
group_by(Collection,ExperimentID) %>%
do(mod = lm(Observed~Interval,data=.)) %>%
mutate(slope = summary(mod)$coeff[2]) %>%
mutate(R2 = summary(mod)$r.squared)%>%
mutate(P=anova(mod)$'Pr(>F)'[1])%>%
mutate(intcp= summary(mod)$coeff[1])%>%
dplyr::select(-mod)library(plyr)--------------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
--------------------------------------------------------------------------------------------------------------
Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
here
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarizeLAR.NEW<-LAR2%>%
dplyr::filter(Rotation.x!="6"|Collection!="1997_2001")%>%
mutate(NAR=slope)%>%
dplyr::filter(Collection!="2014_2019")%>%
dplyr::filter(Collection!="2010_2012")%>%
dplyr::select(Name, Collection, GrowthRotation, NAR,Tmean,Ppm,ExperimentID,ID)
#dplyr::filter(Collection==C("2000_2002","1997_2001","2014_2018","2002_2004"))
LAR.NEW$Collection [1] 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018
[11] 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018 2014_2018
[21] 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001
[31] 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 1997_2001 2002_2004
[41] 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004 2002_2004
[51] 2002_2004 2002_2004 2002_2004 2002_2004 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002
[61] 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002 2000_2002
[71] 2000_2002
Levels: 1997_2001 2000_2002 2002_2004 2010_2012 2014_2018lm_eqn <- function(LAR.NEW){
m <- lm(NAR ~ Tmean, LAR.NEW);
eq <- substitute(italic(y) == Tmean + NAR %.% italic(Tmean)*","~~italic(R)^2~"="~r2,
list(Tmean = format(coef(m)[1], digits = 2),
NAR = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
eqns <- ddply(LAR.NEW,.(ExperimentID),lm_eqn)
#df2 <- data.frame(eq = unclass(eqns), Collection = as.numeric(names(eqns)))
a<-ggplot(LAR.NEW, aes(x=Tmean, y=NAR))+
geom_point()+theme_bw()+xlab("Mean air temperature (°C)")+
geom_text(data = eqns, aes(x = 13, y = 0.48, label = V1), size=4.7,
color = 'black', parse = TRUE)+
ylab(bquote(bold('Node appearance rate ('*node~day^-1*')')))+
scale_x_continuous(limits=c(5,20))+
facet_wrap(~ExperimentID)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"))
#ggsave("D:/R/Pictures/Node/Node apparence rate.png", width=8, height=6, dpi=500)
a
detach(package:plyr)LAR2 #dplyr::filter(Rotation!="6"|Collection!="1997_2001")
LAR2.lm=lm(slope~Tmean,data=LAR2)
summary(LAR2.lm)
Call:
lm(formula = slope ~ Tmean, data = LAR2)
Residuals:
Min 1Q Median 3Q Max
-0.142755 -0.060941 -0.007349 0.056628 0.171460
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.001896 0.035007 0.054 0.957
Tmean 0.018709 0.002579 7.254 3.51e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.0702 on 73 degrees of freedom
Multiple R-squared: 0.4189, Adjusted R-squared: 0.4109
F-statistic: 52.62 on 1 and 73 DF, p-value: 3.514e-10predict(LAR2.lm, LAR2, interval="predict") fit lwr upr
1 0.2893523 0.1480724703 0.4306321
2 0.3120969 0.1701889979 0.4540047
3 0.2619888 0.1211005062 0.4028771
4 0.1869465 0.0450782027 0.3288149
5 0.2000531 0.0585718438 0.3415344
6 0.2513464 0.1105023104 0.3921906
7 0.2765514 0.1355044768 0.4175983
8 0.3349125 0.1921023944 0.4777226
9 0.2993657 0.1578430814 0.4408884
10 0.2525481 0.1117019893 0.3933941
11 0.1935086 0.0518454158 0.3351719
12 0.2392637 0.0983960731 0.3801313
13 0.2969517 0.1554924451 0.4384110
14 0.3221620 0.1798894771 0.4644346
15 0.2352880 0.0943956278 0.3761805
16 0.1333112 -0.0110690730 0.2776915
17 0.1533606 0.0100914352 0.2966298
18 0.2095842 0.0683274321 0.3508410
19 0.2702247 0.1292606681 0.4111888
20 0.3318085 0.1891370263 0.4744800
21 0.2495323 0.1086895917 0.3903749
22 0.2923774 0.1510298770 0.4337250
23 0.3336207 0.1908688777 0.4763725
24 0.3634814 0.2191650559 0.5077977
25 0.2450865 0.1042399689 0.3859330
26 0.1535398 0.0102796064 0.2967999
27 0.2354077 0.0945161478 0.3762992
28 0.3309772 0.1883419635 0.4736124
29 0.3301017 0.1875042479 0.4726991
30 0.2995955 0.1580666437 0.4411244
31 0.1463030 0.0026657522 0.2899402
32 0.2506783 0.1098348849 0.3915217
33 0.2480100 0.1071672463 0.3888528
34 0.2729272 0.1319303996 0.4139241
35 0.3034658 0.1618283601 0.4451032
36 0.2234391 0.0824225637 0.3644557
37 0.1600318 0.0170872149 0.3029764
38 0.2139924 0.0728231885 0.3551617
39 0.2661918 0.1252694018 0.4071142
40 0.2899563 0.1486633616 0.4312493
41 0.3021530 0.1605532780 0.4437527
42 0.2606499 0.1197705610 0.4015293
43 0.1540197 0.0107836082 0.2972558
44 0.1506963 0.0072911582 0.2941014
45 0.2072025 0.0658941893 0.3485109
46 0.2717034 0.1307219139 0.4126849
47 0.3040429 0.1623886825 0.4456972
48 0.2951563 0.1537421606 0.4365704
49 0.2200926 0.0790273555 0.3611578
50 0.1848582 0.0429198350 0.3267965
51 0.1446357 0.0009078732 0.2883635
52 0.2254167 0.0844260563 0.3664073
53 0.2742309 0.1332168195 0.4152449
54 0.3343193 0.1915360585 0.4771025
55 0.2843987 0.1432193579 0.4255780
56 0.2192580 0.0781797125 0.3603363
57 0.1816873 0.0396382547 0.3237363
58 0.1367467 -0.0074288436 0.2809223
59 0.2660152 0.1250944531 0.4069360
60 0.3071611 0.1654126789 0.4489095
61 0.2631297 0.1222331118 0.4040263
62 0.1518678 0.0085229389 0.2952128
63 0.1462936 0.0026559031 0.2899313
64 0.2394569 0.0985902525 0.3803235
65 0.2851066 0.1439137000 0.4262995
66 0.2989340 0.1574229054 0.4404451
67 0.2802534 0.1391481144 0.4213587
68 0.1997933 0.0583052053 0.3412813
69 0.2834632 0.1423014254 0.4246251
70 0.2780814 0.1370112789 0.4191516
71 0.1516538 0.0082979287 0.2950096
72 0.3046530 0.1629807368 0.4463253
73 0.2631297 0.1222331118 0.4040263
74 0.2977827 0.1563019543 0.4392634
75 0.2422188 0.1013641807 0.3830734# LARs<-LAR1%>%
# dplyr::filter(Collection=="2014_2018")
#
# LAR.lm = lm(Observed ~ TTS2, data=LARs)
# LAR.res = resid(LAR.lm)
#
#
# plot(LARs$Clock.Today, LAR.res, ylab="Residuals", xlab="Date")
# abline(0, 0) load Rotation and Growth season
library(plyr)--------------------------------------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
--------------------------------------------------------------------------------------------------------------
Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
here
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise, summarizephyll <- "D:\\R\\"
StartGrazing <- read.table(paste0(phyll, "ExperimentList.txt"),
header = TRUE)
StartGrazing1<-StartGrazing %>%
mutate(GrowthRotation= as.factor(paste0(GrowthSeason,Rotation)))
PhyPp<- merge(StartGrazing1,mergeP,by=c("Name","Collection","GrowthRotation"))
lm_eqn1 <- function(PhyPp){
m <- lm(Phyllo~Ppm , PhyPp);
eq <- substitute(italic(y) == Ppm + Phyllo %.% italic(Ppm)*","~~italic(R)^2~"="~r2,
list(Ppm = format(coef(m)[1], digits = 2),
Phyllo = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
eqs <- ddply(PhyPp,.(Trend,Stage),lm_eqn1)
#df2 <- data.frame(eq = unclass(eqns), Collection = as.numeric(names(eqns)))
#ylab(bquote(bold('LEAR ('*m^2~m^-2~'°Cd'^-1*')')))
PhyPpmergePPhyPp%>%
#dplyr::filter(Rotation!="1")%>%
#dplyr::filter(Rotation!="7")%>%
#dplyr::filter(Name!="Iversen_8Waterirr"|GrowthSeason!="3")%>%
#dplyr::filter(Stage!="Seedling")%>%
dplyr::filter(PhylloTb=="PhylloBr1")%>%
dplyr::filter(Collection!="2010_2012")%>%
ggplot(aes(x=Ppm, y=Phyllo,label=GrowthRotation,color=ID))+geom_text()+theme_bw()+xlab("Mean photoperiod (h)")+ylab(bquote(bold('Phyllochron ('*'°Cd'~'main node'^-1*')')))+
geom_text(data = eqs, aes(x =13, y = 78, label = V1),
color = 'black', parse = TRUE)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+
facet_grid(Trend~Stage)+
#facet_wrap(~Stage,ncol = 2)+
#geom_point(size=2)+
scale_y_continuous(breaks = seq(0, 70, by =20), limits=c(0,78))+
mytheme3+theme(legend.title = element_blank())
#ggsave("D:/R/Pictures/Node/phyll.png", width=8, height=6, dpi=500)
detach(package:plyr)load Rotation and Growth season for poster
library(plyr)
phyll <- "D:\\R\\"
StartGrazing <- read.table(paste0(phyll, "ExperimentList.txt"),
header = TRUE)
StartGrazing1<-StartGrazing %>%
mutate(GrowthRotation= as.factor(paste0(GrowthSeason,Rotation)))
PhyPp<- merge(StartGrazing1,mergeP,by=c("Name","Collection","GrowthRotation"))
lm_eqn1 <- function(PhyPp){
m <- lm(Phyllo~Ppm , PhyPp);
eq <- substitute(italic(y) == Ppm + Phyllo %.% italic(Ppm)*","~~italic(R)^2~"="~r2,
list(Ppm = format(coef(m)[1], digits = 2),
Phyllo = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
eqs <- ddply(PhyPp,.(Trend,Stage),lm_eqn1)
#df2 <- data.frame(eq = unclass(eqns), Collection = as.numeric(names(eqns)))
#ylab(bquote(bold('LEAR ('*m^2~m^-2~'°Cd'^-1*')')))
PhyPp
mergeP
PhyPp%>%
#dplyr::filter(Rotation!="1")%>%
#dplyr::filter(Rotation!="7")%>%
#dplyr::filter(Name!="Iversen_8Waterirr"|GrowthSeason!="3")%>%
#dplyr::filter(Stage!="Seedling")%>%
dplyr::filter(PhylloTb=="PhylloBr1")%>%
dplyr::filter(Collection!="2010_2012")%>%
ggplot(aes(x=Ppm, y=Phyllo,label=GrowthRotation,color=ID))+geom_text()+theme_bw()+xlab("Mean photoperiod (h)")+ylab(bquote(bold('Phyllochron ('*'°Cd'~'main node'^-1*')')))+
geom_text(data = eqs, aes(x =13, y = 78, label = V1),
color = 'black', parse = TRUE)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey")+
facet_grid(Trend~Stage)+
#facet_wrap(~Stage,ncol = 2)+
#geom_point(size=2)+
scale_y_continuous(breaks = seq(0, 70, by =20), limits=c(0,78))+
mytheme3+theme(legend.title = element_blank())
#ggsave("D:/R/Pictures/Node/phyll.png", width=8, height=6, dpi=500)
detach(package:plyr)PhyPpD<-PhyPp%>%
dplyr::filter(Stage=="Regrowth")%>%
dplyr::filter(Trend=="Dec")
mod<-lm(PhyPpD$Phyllo~PhyPpD$Ppm)
summary(mod)
Call:
lm(formula = PhyPpD$Phyllo ~ PhyPpD$Ppm)
Residuals:
Min 1Q Median 3Q Max
-35.075 -8.259 0.246 7.268 46.956
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.4449 3.3782 14.636 < 2e-16 ***
PhyPpD$Ppm -0.9483 0.2477 -3.828 0.000142 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 13.4 on 643 degrees of freedom
Multiple R-squared: 0.02229, Adjusted R-squared: 0.02077
F-statistic: 14.66 on 1 and 643 DF, p-value: 0.0001416PhyPpI<-PhyPp%>%
dplyr::filter(Stage=="Regrowth")%>%
dplyr::filter(Trend!="Dec")
mod<-lm(PhyPpI$Phyllo~PhyPpI$Ppm)
summary(mod)
Call:
lm(formula = PhyPpI$Phyllo ~ PhyPpI$Ppm)
Residuals:
Min 1Q Median 3Q Max
-24.205 -9.945 -0.083 7.226 44.098
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.8304 4.5053 5.067 6.15e-07 ***
PhyPpI$Ppm 0.2081 0.3201 0.650 0.516
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 12.51 on 403 degrees of freedom
Multiple R-squared: 0.001048, Adjusted R-squared: -0.001431
F-statistic: 0.4228 on 1 and 403 DF, p-value: 0.5159Define stats function
- Using Gauch et al. 2003 (Model evaluation by comparison of model-based predictions and measured values. Agron. J. 95, 1442-1446)
Test stats functions used
s <- c(4231.972,3935.604,3779.652,3627.687,3363.499,3230.566,2868.114,2868.827)
m <- c(4987.66,5636.09,4754.06,4114.53,4141.72,3704.06,5142.19,4762.03)
x <- gauchStats(s,m)
tempDf <- data.frame(statName=c("SB","NU","LC","r_MSD","R2"), statValue=x)
# kable(tempDf, digits= 2)
tempDf2 <- data.frame(Predicted=s, Observed=m)
x <- tempDf2 %>%
summarise(
n = n(),
r2 = gauchStats(Predicted,Observed)[5],
# rmse = round(rmse(Predicted,Observed),0),
r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
nse = round(NSE(Predicted,Observed),1),
sb = gauchStats(Predicted,Observed)[1],
nu = gauchStats(Predicted,Observed)[2],
lc = gauchStats(Predicted,Observed)[3]
) %>%
t()
df <- data.frame(stat = row.names(x),statvalue = x[,1])
df %>%
kable(format = "markdown")| stat | statvalue | |
|---|---|---|
| n | n | 8.0 |
| r2 | r2 | 7.1 |
| r_rmse | r_rmse | 28.7 |
| nse | nse | -4.1 |
| sb | sb | 76.5 |
| nu | nu | 5.2 |
| lc | lc | 18.2 |
Load simulated database
create function to read data (Justin’s script)
load address of db
set table to be enquierd
load table into an object
make it a dataframe
change date to corerct format
explore the df
db.address <- "D:\\APSIMX2\\Prototypes\\Lucerne\\LucerneValidation.db"
tableName<-"Report"
DbTable <- GetApsimNGTable(db.address,tableName)
df <- as.data.frame(DbTable)
df$Clock.Today <- ymd_hms(df$Clock.Today)
str(df)'data.frame': 110826 obs. of 246 variables:
$ CheckpointID : int 1 1 1 1 1 1 1 1 1 1 ...
$ SimulationID : int 86 86 86 86 86 86 86 86 86 86 ...
$ Defoliation : chr NA NA NA NA ...
$ Zone : chr "paddock" "paddock" "paddock" "paddock" ...
$ Clock.Today : POSIXct, format: "1994-08-05 12:00:00" "1994-08-06 12:00:00" "1994-08-07 12:00:00" "1994-08-08 12:00:00" ...
$ DiagnosticsVariables.Script.AccumPlantN : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumMineralisation : num 0 0.108 0.243 0.407 0.58 ...
$ DiagnosticsVariables.Script.AccumDenit : num 0 -0.00812 -0.00816 -0.00816 -0.00816 ...
$ DiagnosticsVariables.Script.AccumFert : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumLeach : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumDetach : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.DeltaSoilOMN : num 0 -0.108 -0.243 -0.407 -0.58 ...
$ DiagnosticsVariables.Script.DeltaSurfaceOMN : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.DeltaSoilMineralN : num 0 0.0998 0.2344 0.3989 0.572 ...
$ Lucerne.Root.NSupply.Fixation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Reallocation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Retranslocation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Total : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Uptake : num 0 0 0 0 0 0 0 0 0 0 ...
$ Soil.SoilWater.Eo : num 3.13 3.13 3.29 2.98 2.91 ...
$ Soil.SoilWater.Es : num 3.13 2.48 3.29 2.88 1.58 ...
$ SWC : num 1399 1396 1393 1390 1389 ...
$ DiagnosticsVariables.Script.DUL : num 1495 1495 1495 1495 1495 ...
$ Soil.SoilWater.Drainage : num 0 0 0 0 0 0 0 0 0 0 ...
$ Soil.SoilWater.Runoff : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.OutFlowLat : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumEO : num -3.13 -6.26 -9.55 -12.53 -15.44 ...
$ DiagnosticsVariables.Script.AccumEP : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumES : num -3.13 -5.62 -8.91 -11.79 -13.37 ...
$ DiagnosticsVariables.Script.AccumDrainage : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumRunoff : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumRainfall : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumIrrigation : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumOutflowLat : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.SoilWaterDeficit : num 0 -96.1 -98.6 -101.9 -104.8 ...
$ Lucerne.Grain.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Shell.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ StemWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Stem.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Grain.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Shell.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ LeafWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.TapRoot.Live.StructuralWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.TapRoot.Live.StorageWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ RootWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ RootTotal : num 0 0 0 0 0 0 0 0 0 0 ...
$ TapRoot : num 0 0 0 0 0 0 0 0 0 0 ...
$ lucerne.TapRoot.Senesced.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.TapRoot.MaintenanceRespiration : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.Live.NConc : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Live.NConc : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.WaterUptake : num 0 0 0 0 0 0 0 0 0 0 ...
$ ET : num 3.13 2.48 3.29 2.88 1.58 ...
$ Lucerne.Root.Depth : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.MaintenanceRespiration : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.CoverTotal : num 0 0 0 0 0 0 0 0 0 0 ...
$ NodeNumber : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.CoverDead : num 0 0 0 0 0 0 0 0 0 0 ...
$ LAI : num 0 0 0 0 0 0 0 0 0 0 ...
$ Height : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. : num 0 0 0 0 0 0 0 0 0 0 ...
$ SWmm.1. : num 43 40.8 37.7 35.2 33.9 ...
$ SWmm.2. : num 92.2 92.3 92.4 92.4 92.4 ...
$ SWmm.3. : num 105 104 104 103 103 ...
$ SWmm.4. : num 103 103 103 103 102 ...
$ SWmm.5. : num 96.1 96.2 96.4 96.5 96.6 ...
$ SWmm.6. : num 92 92.1 92.2 92.2 92.3 ...
$ SWmm.7. : num 90 90.1 90.1 90.1 90.2 ...
$ SWmm.8. : num 90 90 90 90 90 ...
$ SWmm.9. : num 90 90 90 90 90 ...
$ SWmm.10. : num 88 88 88 88 88 ...
$ SWmm.11. : num 84 84 84 84.1 84.1 ...
$ SWmm.12. : num 84 84 84 84 84 ...
$ SWmm.13. : num 86 86 86 86 86 ...
$ SWmm.14. : num 88 88 87.9 87.9 87.9 ...
$ SWmm.15. : num 84 84 84 84.1 84.1 ...
$ SWmm.16. : num 84 84 84 84 84 ...
$ SWmm.17. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.18. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.19. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.20. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.21. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.22. : num NA NA NA NA NA NA NA NA NA NA ...
$ SWmm.23. : num NA NA NA NA NA NA NA NA NA NA ...
$ Soil.NO3N.1. : num 10.7 10.8 10.9 11 11.1 ...
$ Soil.NO3N.2. : num 22 22.1 22.1 22.2 22.2 ...
$ Soil.NO3N.3. : num 17.1 17.1 17 17 17 ...
$ Soil.NO3N.4. : num 13.8 13.9 13.9 13.9 14 ...
$ Soil.NO3N.5. : num 10.7 10.8 10.9 10.9 11 ...
$ Soil.NO3N.6. : num 8.6 8.65 8.71 8.77 8.82 ...
$ Soil.NO3N.7. : num 6.58 6.63 6.69 6.74 6.79 ...
$ Soil.NO3N.8. : num 5.67 5.72 5.77 5.82 5.87 ...
$ Soil.NO3N.9. : num 5.98 6.03 6.09 6.14 6.18 ...
$ Soil.NO3N.10. : num 7.83 7.88 7.94 7.99 8.04 ...
$ Soil.NO3N.11. : num 10.3 10.3 10.4 10.4 10.5 ...
$ Soil.NO3N.12. : num 11.9 12 12 12.1 12.1 ...
$ Soil.NO3N.13. : num 13.6 13.7 13.7 13.8 13.8 ...
[list output truncated]summary(df) CheckpointID SimulationID Defoliation Zone Clock.Today
Min. :1 Min. : 1.00 Length:110826 Length:110826 Min. :1979-01-01 12:00:00
1st Qu.:1 1st Qu.: 42.00 Class :character Class :character 1st Qu.:1999-08-11 12:00:00
Median :1 Median : 66.00 Mode :character Mode :character Median :2001-08-28 12:00:00
Mean :1 Mean : 59.99 Mean :2003-01-29 03:05:51
DiagnosticsVariables.Script.AccumPlantN DiagnosticsVariables.Script.AccumMineralisation
Min. : 0.0 Min. : -0.1345
1st Qu.:251.3 1st Qu.: 60.0012
Median :344.7 Median :137.9552
Mean :315.2 Mean :151.3531
DiagnosticsVariables.Script.AccumDenit DiagnosticsVariables.Script.AccumFert
Min. :-124.234 Min. :0
1st Qu.: -22.701 1st Qu.:0
Median : -11.416 Median :0
Mean : -16.857 Mean :0
DiagnosticsVariables.Script.AccumLeach DiagnosticsVariables.Script.AccumDetach
Min. :-101.7592 Min. :0
1st Qu.: -3.1909 1st Qu.:0
Median : -0.7215 Median :0
Mean : -3.7641 Mean :0
DiagnosticsVariables.Script.DeltaSoilOMN DiagnosticsVariables.Script.DeltaSurfaceOMN
Min. :-12180.529 Min. :-16.000
1st Qu.: -230.777 1st Qu.: 0.000
Median : -140.909 Median : 0.000
Mean : -253.349 Mean : -0.362
DiagnosticsVariables.Script.DeltaSoilMineralN Lucerne.Root.NSupply.Fixation Lucerne.Root.NSupply.Reallocation
Min. :-260.0 Min. :0 Min. :0
1st Qu.:-252.0 1st Qu.:0 1st Qu.:0
Median :-243.1 Median :0 Median :0
Mean :-185.0 Mean :0 Mean :0
Lucerne.Root.NSupply.Retranslocation Lucerne.Root.NSupply.Total Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo
Min. :0 Min. :0 Min. :0 Min. : 0.05417
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.: 1.23570
Median :0 Median :0 Median :0 Median : 2.52347
Mean :0 Mean :0 Mean :0 Mean : 2.91491
Soil.SoilWater.Es SWC DiagnosticsVariables.Script.DUL Soil.SoilWater.Drainage
Min. :0.0000 Min. : 78.98 Min. : 103 Min. : 0.0000
1st Qu.:0.3055 1st Qu.: 559.79 1st Qu.: 723 1st Qu.: 0.0000
Median :0.5312 Median : 702.73 Median : 744 Median : 0.0000
Mean :0.7451 Mean : 663.84 Mean : 780 Mean : 0.1965
Soil.SoilWater.Runoff DiagnosticsVariables.Script.OutFlowLat DiagnosticsVariables.Script.AccumEO
Min. : 0.000000 Min. :0 Min. :-8991.953
1st Qu.: 0.000000 1st Qu.:0 1st Qu.:-3045.972
Median : 0.000000 Median :0 Median :-1796.676
Mean : 0.004499 Mean :0 Mean :-2101.595
DiagnosticsVariables.Script.AccumEP DiagnosticsVariables.Script.AccumES
Min. :-2949.0 Min. :-1974.892
1st Qu.:-1216.2 1st Qu.: -757.478
Median : -744.5 Median : -458.858
Mean : -789.2 Mean : -541.785
DiagnosticsVariables.Script.AccumDrainage DiagnosticsVariables.Script.AccumRunoff
Min. :-1038.03 Min. :-153.814
1st Qu.: -241.96 1st Qu.: 0.000
Median : -16.83 Median : 0.000
Mean : -124.30 Mean : -3.254
DiagnosticsVariables.Script.AccumRainfall DiagnosticsVariables.Script.AccumIrrigation
Min. : 0.0 Min. :0
1st Qu.: 325.0 1st Qu.:0
Median : 856.2 Median :0
Mean :1007.1 Mean :0
DiagnosticsVariables.Script.AccumOutflowLat DiagnosticsVariables.Script.SoilWaterDeficit Lucerne.Grain.Live.Wt
Min. :0 Min. :-624.63 Min. : 0.00000
1st Qu.:0 1st Qu.:-190.72 1st Qu.: 0.00000
Median :0 Median : -66.92 Median : 0.00000
Mean :0 Mean :-115.99 Mean : 5.32138
Lucerne.Shell.Live.Wt StemWt Lucerne.Stem.Live.Wt Lucerne.Grain.Live.N Lucerne.Shell.Live.N
Min. : 0.00000 Min. : 0.0 Min. : 0.00 Min. :0.0000000 Min. :0.0000000
1st Qu.: 0.00000 1st Qu.: 195.6 1st Qu.: 19.56 1st Qu.:0.0000000 1st Qu.:0.0000000
Median : 0.00000 Median : 955.3 Median : 95.53 Median :0.0000000 Median :0.0000000
Mean : 5.32138 Mean : 6096.9 Mean : 609.69 Mean :0.1596415 Mean :0.1596415
LeafWt Lucerne.TapRoot.Live.StructuralWt Lucerne.TapRoot.Live.StorageWt RootWt
Min. : 0.0 Min. : 0.0 Min. : 0.0000 Min. : 0
1st Qu.: 357.7 1st Qu.:400.0 1st Qu.: 0.0001 1st Qu.:5000
Median :1079.7 Median :500.0 Median : 25.9355 Median :5200
Mean :1600.0 Mean :442.5 Mean : 87.9577 Mean :5304
RootTotal TapRoot lucerne.TapRoot.Senesced.Wt Lucerne.TapRoot.MaintenanceRespiration
Min. : 0 Min. : 0 Min. :0 Min. : 0.0000
1st Qu.:5000 1st Qu.:5000 1st Qu.:0 1st Qu.: 0.0000
Median :5200 Median :5200 Median :0 Median : 0.0597
Mean :5304 Mean :5304 Mean :0 Mean : 1.5448
Lucerne.Leaf.Live.N Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc
Min. :0.000e+00 Min. : 0.000 Min. :0.000e+00 Min. :0.000000
1st Qu.:0.000e+00 1st Qu.: 2.087 1st Qu.:0.000e+00 1st Qu.:0.009988
Median :0.000e+00 Median : 5.027 Median :0.000e+00 Median :0.009997
Mean :8.088e-05 Mean : 5.541 Mean :4.702e-04 Mean :0.009885
Lucerne.Root.WaterUptake ET Lucerne.Root.Depth Lucerne.Root.MaintenanceRespiration
Min. :0.0000 Min. : 0.0000 Min. : 0 Min. :0
1st Qu.:0.0734 1st Qu.: 0.7993 1st Qu.:2300 1st Qu.:0
Median :0.5486 Median : 1.7187 Median :2300 Median :0
Mean :1.0745 Mean : 2.1398 Mean :2085 Mean :0
Lucerne.Leaf.CoverTotal NodeNumber Lucerne.Leaf.CoverDead LAI Height
Min. :0.0000 Min. : 0.000 Min. :0 Min. : 0.000 Min. : 0.0
1st Qu.:0.2086 1st Qu.: 3.298 1st Qu.:0 1st Qu.: 1.215 1st Qu.: 102.6
Median :0.7899 Median : 7.983 Median :0 Median : 3.344 Median : 297.9
Mean :0.6225 Mean :13.136 Mean :0 Mean : 7.504 Mean : 400.8
Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. SWmm.1. SWmm.2. SWmm.3.
Min. : 0.0000 Min. : 1.00 Min. : 2.00 Min. : 5.00
1st Qu.: 0.8781 1st Qu.:23.12 1st Qu.: 26.02 1st Qu.: 24.24
Median : 3.8486 Median :29.89 Median : 31.84 Median : 26.40
Mean : 6.6870 Mean :28.95 Mean : 30.64 Mean : 30.83
SWmm.4. SWmm.5. SWmm.6. SWmm.7. SWmm.8. SWmm.9.
Min. : 7.79 Min. : 7.338 Min. : 7.281 Min. : 7.219 Min. : 7.33 Min. : 7.22
1st Qu.: 24.02 1st Qu.: 25.457 1st Qu.: 25.312 1st Qu.: 22.747 1st Qu.: 22.27 1st Qu.: 21.68
Median : 26.49 Median : 27.078 Median : 29.510 Median : 26.000 Median : 28.17 Median : 29.00
Mean : 31.35 Mean : 32.420 Mean : 33.445 Mean : 32.783 Mean : 34.25 Mean : 34.08
SWmm.10. SWmm.11. SWmm.12. SWmm.13. SWmm.14. SWmm.15.
Min. : 7.303 Min. : 7.59 Min. : 8.58 Min. : 8.68 Min. : 9.10 Min. :10.00
1st Qu.: 22.529 1st Qu.: 22.81 1st Qu.: 23.65 1st Qu.:23.67 1st Qu.:23.62 1st Qu.:23.65
Median : 30.729 Median : 31.30 Median : 30.97 Median :29.35 Median :30.16 Median :32.59
Mean : 34.866 Mean : 32.66 Mean : 29.68 Mean :28.74 Mean :29.24 Mean :30.03
SWmm.16. SWmm.17. SWmm.18. SWmm.19. SWmm.20. SWmm.21.
Min. :10.23 Min. : 9.83 Min. : 9.78 Min. : 9.72 Min. : 9.99 Min. :10.18
1st Qu.:24.61 1st Qu.:25.62 1st Qu.:25.67 1st Qu.:25.77 1st Qu.:25.93 1st Qu.:25.32
Median :32.81 Median :31.89 Median :32.37 Median :32.97 Median :32.98 Median :32.89
Mean :30.86 Mean :30.17 Mean :30.52 Mean :30.80 Mean :30.89 Mean :30.91
SWmm.22. SWmm.23. Soil.NO3N.1. Soil.NO3N.2. Soil.NO3N.3. Soil.NO3N.4.
Min. :10.69 Min. :15.00 Min. : 0.0609 Min. : 0.1309 Min. : 0.2316 Min. : 0.2864
1st Qu.:24.94 1st Qu.:27.00 1st Qu.: 1.1346 1st Qu.: 1.2847 1st Qu.: 1.3668 1st Qu.: 1.1534
Median :32.80 Median :33.82 Median : 2.1805 Median : 2.2041 Median : 2.2171 Median : 1.7233
Mean :30.52 Mean :31.99 Mean : 12.3277 Mean : 6.1282 Mean : 5.4553 Mean : 3.8418
Soil.NO3N.5. Soil.NO3N.6. Soil.NO3N.7. Soil.NO3N.8. Soil.NO3N.9. Soil.NO3N.10.
Min. : 0.359 Min. : 0.467 Min. : 0.428 Min. : 0.436 Min. : 0.315 Min. : 0.000
1st Qu.: 1.172 1st Qu.: 1.211 1st Qu.: 1.024 1st Qu.: 1.006 1st Qu.: 0.961 1st Qu.: 0.658
Median : 1.694 Median : 1.751 Median : 1.469 Median : 1.414 Median : 1.277 Median : 0.891
Mean : 3.334 Mean : 2.943 Mean : 2.286 Mean : 2.045 Mean : 1.906 Mean : 1.570
Soil.NO3N.11. Soil.NO3N.12. Soil.NO3N.13. Soil.NO3N.14. Soil.NO3N.15. Soil.NO3N.16.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.068
1st Qu.: 0.531 1st Qu.: 0.466 1st Qu.: 0.410 1st Qu.: 0.346 1st Qu.: 0.339 1st Qu.: 0.407
Median : 0.767 Median : 0.657 Median : 0.585 Median : 0.556 Median : 0.491 Median : 0.506
Mean : 1.506 Mean : 1.332 Mean : 1.250 Mean : 1.177 Mean : 1.114 Mean : 1.103
Soil.NO3N.17. Soil.NO3N.18. Soil.NO3N.19. Soil.NO3N.20. Soil.NO3N.21. Soil.NO3N.22.
Min. :0.133 Min. :0.098 Min. :0.076 Min. :0.098 Min. :0.098 Min. :0.095
1st Qu.:0.356 1st Qu.:0.325 1st Qu.:0.304 1st Qu.:0.287 1st Qu.:0.268 1st Qu.:0.261
Median :0.465 Median :0.424 Median :0.402 Median :0.389 Median :0.392 Median :0.469
Mean :0.863 Mean :0.795 Mean :0.754 Mean :0.730 Mean :0.727 Mean :0.746
Soil.NO3N.23. DiagnosticsVariables.Script.SoilNitrogenContent Lucerne.Arbitrator.N.TotalPlantDemand
Min. :0.092 Min. : 0.00 Min. :0.000000
1st Qu.:0.254 1st Qu.: 19.81 1st Qu.:0.004248
Median :0.520 Median : 26.10 Median :0.077676
Mean :0.763 Mean : 54.96 Mean :0.162730
DiagnosticsVariables.Script.FomN DiagnosticsVariables.Script.HumN DiagnosticsVariables.Script.BiomN
Min. : 0.0000 Min. : 0 Min. : 0.0
1st Qu.: 0.3819 1st Qu.:22701 1st Qu.: 99.5
Median : 1.1118 Median :22799 Median :128.2
Mean : 3.4498 Mean :20429 Mean :128.8
DiagnosticsVariables.Script.DltNMinRes DiagnosticsVariables.Script.DltNMinTot Lucerne.Leaf.Fw
Min. :-2.898e-01 Min. :-0.02068 Min. :0.0000
1st Qu.: 0.000e+00 1st Qu.: 0.08718 1st Qu.:1.0000
Median : 0.000e+00 Median : 0.16660 Median :1.0000
Mean :-3.008e-05 Mean : 0.19528 Mean :0.9052
Lucerne.Leaf.Fn Lucerne.Phenology.CurrentPhaseName Lucerne.Phenology.CurrentStageName
Min. :0.0000000 Length:110826 Length:110826
1st Qu.:0.0000000 Class :character Class :character
Median :0.0000000 Mode :character Mode :character
Mean :0.0690763
Lucerne.Phenology.Stage Lucerne.Phenology.DaysAfterCutting.Value..
Min. :1.000 Min. : 0.00
1st Qu.:4.433 1st Qu.: 17.00
Median :5.082 Median : 37.00
Mean :5.563 Mean : 34.82
Lucerne.Phenology.FloweringDaysAfterCutting.Value.. Lucerne.Pod.Wt Lucerne.Pod.N shootbiomass
Min. : 0.00 Min. : 0.00000 Min. :0.000000 Min. : 0.0
1st Qu.: 37.00 1st Qu.: 0.00000 1st Qu.:0.000000 1st Qu.: 556.5
Median : 46.00 Median : 0.00000 Median :0.000000 Median : 2027.8
Mean : 44.78 Mean : 10.64277 Mean :0.319283 Mean : 7803.4
Lucerne.Leaf.LAIFunction.Value.. Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2.
Min. : 0.000 Min. :0.00000 Min. :0.000000
1st Qu.: 1.215 1st Qu.:0.02057 1st Qu.:0.004291
Median : 3.344 Median :0.03875 Median :0.012039
Mean : 7.504 Mean :0.03834 Mean :0.019694
Lucerne.Root.LengthDensity.3. Lucerne.Root.LengthDensity.4. Lucerne.Root.LengthDensity.5.
Min. :0.000000 Min. :0.000000 Min. :0.000000
1st Qu.:0.002882 1st Qu.:0.002627 1st Qu.:0.002886
Median :0.008293 Median :0.007560 Median :0.007810
Mean :0.012066 Mean :0.009343 Mean :0.009403
Lucerne.Root.LengthDensity.6. Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8.
Min. :0.0000 Min. :0.0000 Min. :0.000
1st Qu.:0.0030 1st Qu.:0.0029 1st Qu.:0.003
Median :0.0087 Median :0.0086 Median :0.009
Mean :0.0100 Mean :0.0097 Mean :0.009
Lucerne.Root.LengthDensity.9. Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.003 1st Qu.:0.003 1st Qu.:0.003
Median :0.008 Median :0.008 Median :0.007
Mean :0.008 Mean :0.008 Mean :0.008
Lucerne.Root.LengthDensity.12. Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.003 1st Qu.:0.002 1st Qu.:0.002
Median :0.006 Median :0.006 Median :0.006
Mean :0.007 Mean :0.007 Mean :0.007
Lucerne.Root.LengthDensity.15. Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.002 1st Qu.:0.002 1st Qu.:0.002
Median :0.005 Median :0.005 Median :0.003
Mean :0.006 Mean :0.005 Mean :0.004
Lucerne.Root.LengthDensity.18. Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.001 1st Qu.:0.001
Median :0.003 Median :0.003 Median :0.003
Mean :0.004 Mean :0.003 Mean :0.004
Lucerne.Root.LengthDensity.21. Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.001 1st Qu.:0.001
Median :0.003 Median :0.005 Median :0.006
Mean :0.003 Mean :0.006 Mean :0.007
Soil.SoilWater.WaterTable Lucerne.AboveGround.Wt Lucerne.AboveGround.N Soil.SoilWater.ESW.1.
Min. : 0.784 Min. : 0.00 Min. : 0.0000 Min. : 0.00
1st Qu.: 600.000 1st Qu.: 55.65 1st Qu.: 0.5872 1st Qu.:13.31
Median :2300.000 Median : 202.78 Median : 2.8688 Median :20.96
Mean :1753.611 Mean : 780.34 Mean : 18.5285 Mean :18.56
Soil.SoilWater.ESW.2. Soil.SoilWater.ESW.3. Soil.SoilWater.ESW.4. Soil.SoilWater.ESW.5. Soil.SoilWater.ESW.6.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:14.61 1st Qu.:14.45 1st Qu.:12.20 1st Qu.: 11.16 1st Qu.:11.70
Median :22.85 Median :18.13 Median :18.03 Median : 19.91 Median :20.59
Mean :19.47 Mean :17.59 Mean :16.44 Mean : 16.28 Mean :17.13
Soil.SoilWater.ESW.7. Soil.SoilWater.ESW.8. Soil.SoilWater.ESW.9. Soil.SoilWater.ESW.10.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 9.127 1st Qu.: 6.943 1st Qu.: 7.811 1st Qu.: 9.083
Median :17.913 Median :19.681 Median :19.556 Median :19.988
Mean :15.805 Mean :16.513 Mean :16.223 Mean :17.092
Soil.SoilWater.ESW.11. Soil.SoilWater.ESW.12. Soil.SoilWater.ESW.13. Soil.SoilWater.ESW.14.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:10.73 1st Qu.:13.54 1st Qu.:12.99 1st Qu.:13.10
Median :20.70 Median :22.13 Median :20.00 Median :21.31
Mean :17.86 Mean :19.26 Mean :17.91 Mean :18.38
Soil.SoilWater.ESW.15. Soil.SoilWater.ESW.16. Soil.SoilWater.ESW.17. Soil.SoilWater.ESW.18.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:13.55 1st Qu.:13.35 1st Qu.:16.02 1st Qu.:16.02
Median :23.41 Median :21.74 Median :21.73 Median :22.66
Mean :19.19 Mean :19.11 Mean :19.68 Mean :20.02
Soil.SoilWater.ESW.19. Soil.SoilWater.ESW.20. Soil.SoilWater.ESW.21. Soil.SoilWater.ESW.22.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:16.02 1st Qu.:17.03 1st Qu.:14.88 1st Qu.:14.01
Median :23.44 Median :24.24 Median :23.17 Median :22.96
Mean :20.30 Mean :21.21 Mean :20.30 Mean :19.90
Soil.SoilWater.ESW.23. Water SowingDate FD SD1
Min. : 6.00 Length:110826 Length:110826 Length:110826 Length:110826
1st Qu.:16.10 Class :character Class :character Class :character Class :character
Median :23.09 Mode :character Mode :character Mode :character Mode :character
Mean :21.89
LL SS Cutting Factors Soil.OutputLayers.SWmm.1.
Length:110826 Length:110826 Length:110826 Length:110826 Min. : 3.00
Class :character Class :character Class :character Class :character 1st Qu.: 15.36
Mode :character Mode :character Mode :character Mode :character Median : 22.76
Mean : 25.89
Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3. Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5.
Min. : 21.00 Min. : 42.09 Min. : 0.00 Min. : 51.33
1st Qu.: 28.25 1st Qu.: 54.79 1st Qu.: 50.99 1st Qu.: 86.04
Median : 61.62 Median : 71.16 Median : 80.00 Median :118.70
Mean : 74.09 Mean : 86.21 Mean : 85.57 Mean :113.74
Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7. Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1.
Min. : 54.78 Min. : 57.97 Min. : 60.88 Min. :0.03
1st Qu.: 74.01 1st Qu.: 90.00 1st Qu.: 88.00 1st Qu.:0.08
Median : 84.47 Median : 90.73 Median : 90.19 Median :0.18
Mean :102.25 Mean :107.72 Mean :136.96 Mean :0.20
Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3. Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5.
Min. :0.05 Min. :0.05 Min. :0.00 Min. :0.22
1st Qu.:0.09 1st Qu.:0.16 1st Qu.:0.20 1st Qu.:0.23
Median :0.22 Median :0.23 Median :0.25 Median :0.29
Mean :0.23 Mean :0.24 Mean :0.25 Mean :0.29
Soil.OutputLayers.SW.6. Soil.OutputLayers.SW.7. Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9.
Min. :0.19 Min. :0.22 Min. :0.22 Min. :63.00
1st Qu.:0.19 1st Qu.:0.22 1st Qu.:0.22 1st Qu.:63.34
Median :0.28 Median :0.29 Median :0.31 Median :63.70
Mean :0.28 Mean :0.30 Mean :0.30 Mean :63.66
Soil.OutputLayers.SWmm.10. Soil.OutputLayers.SWmm.11. Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13.
Min. :64.01 Min. :64.00 Min. :60.10 Min. :58.00
1st Qu.:64.33 1st Qu.:64.00 1st Qu.:60.15 1st Qu.:58.00
Median :64.77 Median :64.00 Median :60.29 Median :58.00
Mean :64.87 Mean :64.35 Mean :60.69 Mean :58.00
Soil.OutputLayers.SWmm.14. Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17.
Min. :54.00 Min. :54.00 Min. :54.00 Min. :54.50
1st Qu.:54.01 1st Qu.:54.01 1st Qu.:54.34 1st Qu.:54.57
Median :54.20 Median :54.06 Median :54.43 Median :54.67
Mean :54.23 Mean :54.05 Mean :54.40 Mean :55.06
Soil.OutputLayers.SWmm.18. Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20. Soil.OutputLayers.SWmm.21.
Min. :58.01 Min. :60.84 Min. :61.20 Min. :58.82
1st Qu.:58.81 1st Qu.:61.69 1st Qu.:62.48 1st Qu.:60.49
Median :59.11 Median :62.50 Median :63.82 Median :63.13
Mean :59.03 Mean :62.46 Mean :63.96 Mean :63.65
Soil.OutputLayers.SWmm.22. Soil.OutputLayers.SWmm.23. Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10.
Min. :51.15 Min. :0 Min. :0.32 Min. :0.32
1st Qu.:51.55 1st Qu.:0 1st Qu.:0.32 1st Qu.:0.32
Median :53.14 Median :0 Median :0.32 Median :0.32
Mean :56.05 Mean :0 Mean :0.32 Mean :0.32
Soil.OutputLayers.SW.11. Soil.OutputLayers.SW.12. Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14.
Min. :0.32 Min. :0.30 Min. :0.29 Min. :0.27
1st Qu.:0.32 1st Qu.:0.30 1st Qu.:0.29 1st Qu.:0.27
Median :0.32 Median :0.30 Median :0.29 Median :0.27
Mean :0.32 Mean :0.30 Mean :0.29 Mean :0.27
Soil.OutputLayers.SW.15. Soil.OutputLayers.SW.16. Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18.
Min. :0.27 Min. :0.27 Min. :0.27 Min. :0.29
1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.29
Median :0.27 Median :0.27 Median :0.27 Median :0.30
Mean :0.27 Mean :0.27 Mean :0.28 Mean :0.30
Soil.OutputLayers.SW.19. Soil.OutputLayers.SW.20. Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22.
Min. :0.30 Min. :0.31 Min. :0.29 Min. :0.26
1st Qu.:0.31 1st Qu.:0.31 1st Qu.:0.30 1st Qu.:0.26
Median :0.31 Median :0.32 Median :0.32 Median :0.27
Mean :0.31 Mean :0.32 Mean :0.32 Mean :0.28
Soil.OutputLayers.SW.23. SWmm.24. SWmm.25. SWmm.26. SWmm.27. Soil.NO3N.24.
Min. :0 Min. :58.00 Min. :58.00 Min. :58.00 Min. :58.00 Min. :0.05
1st Qu.:0 1st Qu.:58.63 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:0.22
Median :0 Median :60.00 Median :60.00 Median :60.00 Median :60.00 Median :0.28
Mean :0 Mean :59.37 Mean :59.76 Mean :59.75 Mean :59.75 Mean :1.11
Soil.NO3N.25. Soil.NO3N.26. Soil.NO3N.27. Lucerne.Root.LengthDensity.24.
Min. :0.00 Min. :0.00 Min. :0.00 Min. :0
1st Qu.:0.02 1st Qu.:0.02 1st Qu.:0.02 1st Qu.:0
Median :0.19 Median :0.19 Median :0.19 Median :0
Mean :1.03 Mean :1.03 Mean :1.03 Mean :0
Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26. Lucerne.Root.LengthDensity.27.
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
Soil.SoilWater.ESW.24. Soil.SoilWater.ESW.25. Soil.SoilWater.ESW.26. Soil.SoilWater.ESW.27.
Min. :14.00 Min. :14.00 Min. :14.00 Min. :14.00
1st Qu.:14.63 1st Qu.:16.00 1st Qu.:16.00 1st Qu.:16.00
Median :16.00 Median :16.00 Median :16.00 Median :16.00
Mean :15.37 Mean :15.76 Mean :15.75 Mean :15.75
[ reached getOption("max.print") -- omitted 3 rows ]head(df) # simulation resultsget sim names (different table)
merge names
remove unecessary variables
simNameDf <- as.data.frame (GetApsimNGTable(db.address,"_Simulations"))
myDb <- merge(df, simNameDf, by.x= c("SimulationID"), by.y= c("ID"))
#str(myDb)
head(myDb)summary(myDb) SimulationID CheckpointID Defoliation Zone Clock.Today
Min. : 1.00 Min. :1 Length:110826 Length:110826 Min. :1979-01-01 12:00:00
1st Qu.: 42.00 1st Qu.:1 Class :character Class :character 1st Qu.:1999-08-11 12:00:00
Median : 66.00 Median :1 Mode :character Mode :character Median :2001-08-28 12:00:00
Mean : 59.99 Mean :1 Mean :2003-01-29 03:05:51
DiagnosticsVariables.Script.AccumPlantN DiagnosticsVariables.Script.AccumMineralisation
Min. : 0.0 Min. : -0.1345
1st Qu.:251.3 1st Qu.: 60.0012
Median :344.7 Median :137.9552
Mean :315.2 Mean :151.3531
DiagnosticsVariables.Script.AccumDenit DiagnosticsVariables.Script.AccumFert
Min. :-124.234 Min. :0
1st Qu.: -22.701 1st Qu.:0
Median : -11.416 Median :0
Mean : -16.857 Mean :0
DiagnosticsVariables.Script.AccumLeach DiagnosticsVariables.Script.AccumDetach
Min. :-101.7592 Min. :0
1st Qu.: -3.1909 1st Qu.:0
Median : -0.7215 Median :0
Mean : -3.7641 Mean :0
DiagnosticsVariables.Script.DeltaSoilOMN DiagnosticsVariables.Script.DeltaSurfaceOMN
Min. :-12180.529 Min. :-16.000
1st Qu.: -230.777 1st Qu.: 0.000
Median : -140.909 Median : 0.000
Mean : -253.349 Mean : -0.362
DiagnosticsVariables.Script.DeltaSoilMineralN Lucerne.Root.NSupply.Fixation Lucerne.Root.NSupply.Reallocation
Min. :-260.0 Min. :0 Min. :0
1st Qu.:-252.0 1st Qu.:0 1st Qu.:0
Median :-243.1 Median :0 Median :0
Mean :-185.0 Mean :0 Mean :0
Lucerne.Root.NSupply.Retranslocation Lucerne.Root.NSupply.Total Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo
Min. :0 Min. :0 Min. :0 Min. : 0.05417
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.: 1.23570
Median :0 Median :0 Median :0 Median : 2.52347
Mean :0 Mean :0 Mean :0 Mean : 2.91491
Soil.SoilWater.Es SWC DiagnosticsVariables.Script.DUL Soil.SoilWater.Drainage
Min. :0.0000 Min. : 78.98 Min. : 103 Min. : 0.0000
1st Qu.:0.3055 1st Qu.: 559.79 1st Qu.: 723 1st Qu.: 0.0000
Median :0.5312 Median : 702.73 Median : 744 Median : 0.0000
Mean :0.7451 Mean : 663.84 Mean : 780 Mean : 0.1965
Soil.SoilWater.Runoff DiagnosticsVariables.Script.OutFlowLat DiagnosticsVariables.Script.AccumEO
Min. : 0.000000 Min. :0 Min. :-8991.953
1st Qu.: 0.000000 1st Qu.:0 1st Qu.:-3045.972
Median : 0.000000 Median :0 Median :-1796.676
Mean : 0.004499 Mean :0 Mean :-2101.595
DiagnosticsVariables.Script.AccumEP DiagnosticsVariables.Script.AccumES
Min. :-2949.0 Min. :-1974.892
1st Qu.:-1216.2 1st Qu.: -757.478
Median : -744.5 Median : -458.858
Mean : -789.2 Mean : -541.785
DiagnosticsVariables.Script.AccumDrainage DiagnosticsVariables.Script.AccumRunoff
Min. :-1038.03 Min. :-153.814
1st Qu.: -241.96 1st Qu.: 0.000
Median : -16.83 Median : 0.000
Mean : -124.30 Mean : -3.254
DiagnosticsVariables.Script.AccumRainfall DiagnosticsVariables.Script.AccumIrrigation
Min. : 0.0 Min. :0
1st Qu.: 325.0 1st Qu.:0
Median : 856.2 Median :0
Mean :1007.1 Mean :0
DiagnosticsVariables.Script.AccumOutflowLat DiagnosticsVariables.Script.SoilWaterDeficit Lucerne.Grain.Live.Wt
Min. :0 Min. :-624.63 Min. : 0.00000
1st Qu.:0 1st Qu.:-190.72 1st Qu.: 0.00000
Median :0 Median : -66.92 Median : 0.00000
Mean :0 Mean :-115.99 Mean : 5.32138
Lucerne.Shell.Live.Wt StemWt Lucerne.Stem.Live.Wt Lucerne.Grain.Live.N Lucerne.Shell.Live.N
Min. : 0.00000 Min. : 0.0 Min. : 0.00 Min. :0.0000000 Min. :0.0000000
1st Qu.: 0.00000 1st Qu.: 195.6 1st Qu.: 19.56 1st Qu.:0.0000000 1st Qu.:0.0000000
Median : 0.00000 Median : 955.3 Median : 95.53 Median :0.0000000 Median :0.0000000
Mean : 5.32138 Mean : 6096.9 Mean : 609.69 Mean :0.1596415 Mean :0.1596415
LeafWt Lucerne.TapRoot.Live.StructuralWt Lucerne.TapRoot.Live.StorageWt RootWt
Min. : 0.0 Min. : 0.0 Min. : 0.0000 Min. : 0
1st Qu.: 357.7 1st Qu.:400.0 1st Qu.: 0.0001 1st Qu.:5000
Median :1079.7 Median :500.0 Median : 25.9355 Median :5200
Mean :1600.0 Mean :442.5 Mean : 87.9577 Mean :5304
RootTotal TapRoot lucerne.TapRoot.Senesced.Wt Lucerne.TapRoot.MaintenanceRespiration
Min. : 0 Min. : 0 Min. :0 Min. : 0.0000
1st Qu.:5000 1st Qu.:5000 1st Qu.:0 1st Qu.: 0.0000
Median :5200 Median :5200 Median :0 Median : 0.0597
Mean :5304 Mean :5304 Mean :0 Mean : 1.5448
Lucerne.Leaf.Live.N Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc
Min. :0.000e+00 Min. : 0.000 Min. :0.000e+00 Min. :0.000000
1st Qu.:0.000e+00 1st Qu.: 2.087 1st Qu.:0.000e+00 1st Qu.:0.009988
Median :0.000e+00 Median : 5.027 Median :0.000e+00 Median :0.009997
Mean :8.088e-05 Mean : 5.541 Mean :4.702e-04 Mean :0.009885
Lucerne.Root.WaterUptake ET Lucerne.Root.Depth Lucerne.Root.MaintenanceRespiration
Min. :0.0000 Min. : 0.0000 Min. : 0 Min. :0
1st Qu.:0.0734 1st Qu.: 0.7993 1st Qu.:2300 1st Qu.:0
Median :0.5486 Median : 1.7187 Median :2300 Median :0
Mean :1.0745 Mean : 2.1398 Mean :2085 Mean :0
Lucerne.Leaf.CoverTotal NodeNumber Lucerne.Leaf.CoverDead LAI Height
Min. :0.0000 Min. : 0.000 Min. :0 Min. : 0.000 Min. : 0.0
1st Qu.:0.2086 1st Qu.: 3.298 1st Qu.:0 1st Qu.: 1.215 1st Qu.: 102.6
Median :0.7899 Median : 7.983 Median :0 Median : 3.344 Median : 297.9
Mean :0.6225 Mean :13.136 Mean :0 Mean : 7.504 Mean : 400.8
Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. SWmm.1. SWmm.2. SWmm.3.
Min. : 0.0000 Min. : 1.00 Min. : 2.00 Min. : 5.00
1st Qu.: 0.8781 1st Qu.:23.12 1st Qu.: 26.02 1st Qu.: 24.24
Median : 3.8486 Median :29.89 Median : 31.84 Median : 26.40
Mean : 6.6870 Mean :28.95 Mean : 30.64 Mean : 30.83
SWmm.4. SWmm.5. SWmm.6. SWmm.7. SWmm.8. SWmm.9.
Min. : 7.79 Min. : 7.338 Min. : 7.281 Min. : 7.219 Min. : 7.33 Min. : 7.22
1st Qu.: 24.02 1st Qu.: 25.457 1st Qu.: 25.312 1st Qu.: 22.747 1st Qu.: 22.27 1st Qu.: 21.68
Median : 26.49 Median : 27.078 Median : 29.510 Median : 26.000 Median : 28.17 Median : 29.00
Mean : 31.35 Mean : 32.420 Mean : 33.445 Mean : 32.783 Mean : 34.25 Mean : 34.08
SWmm.10. SWmm.11. SWmm.12. SWmm.13. SWmm.14. SWmm.15.
Min. : 7.303 Min. : 7.59 Min. : 8.58 Min. : 8.68 Min. : 9.10 Min. :10.00
1st Qu.: 22.529 1st Qu.: 22.81 1st Qu.: 23.65 1st Qu.:23.67 1st Qu.:23.62 1st Qu.:23.65
Median : 30.729 Median : 31.30 Median : 30.97 Median :29.35 Median :30.16 Median :32.59
Mean : 34.866 Mean : 32.66 Mean : 29.68 Mean :28.74 Mean :29.24 Mean :30.03
SWmm.16. SWmm.17. SWmm.18. SWmm.19. SWmm.20. SWmm.21.
Min. :10.23 Min. : 9.83 Min. : 9.78 Min. : 9.72 Min. : 9.99 Min. :10.18
1st Qu.:24.61 1st Qu.:25.62 1st Qu.:25.67 1st Qu.:25.77 1st Qu.:25.93 1st Qu.:25.32
Median :32.81 Median :31.89 Median :32.37 Median :32.97 Median :32.98 Median :32.89
Mean :30.86 Mean :30.17 Mean :30.52 Mean :30.80 Mean :30.89 Mean :30.91
SWmm.22. SWmm.23. Soil.NO3N.1. Soil.NO3N.2. Soil.NO3N.3. Soil.NO3N.4.
Min. :10.69 Min. :15.00 Min. : 0.0609 Min. : 0.1309 Min. : 0.2316 Min. : 0.2864
1st Qu.:24.94 1st Qu.:27.00 1st Qu.: 1.1346 1st Qu.: 1.2847 1st Qu.: 1.3668 1st Qu.: 1.1534
Median :32.80 Median :33.82 Median : 2.1805 Median : 2.2041 Median : 2.2171 Median : 1.7233
Mean :30.52 Mean :31.99 Mean : 12.3277 Mean : 6.1282 Mean : 5.4553 Mean : 3.8418
Soil.NO3N.5. Soil.NO3N.6. Soil.NO3N.7. Soil.NO3N.8. Soil.NO3N.9. Soil.NO3N.10.
Min. : 0.359 Min. : 0.467 Min. : 0.428 Min. : 0.436 Min. : 0.315 Min. : 0.000
1st Qu.: 1.172 1st Qu.: 1.211 1st Qu.: 1.024 1st Qu.: 1.006 1st Qu.: 0.961 1st Qu.: 0.658
Median : 1.694 Median : 1.751 Median : 1.469 Median : 1.414 Median : 1.277 Median : 0.891
Mean : 3.334 Mean : 2.943 Mean : 2.286 Mean : 2.045 Mean : 1.906 Mean : 1.570
Soil.NO3N.11. Soil.NO3N.12. Soil.NO3N.13. Soil.NO3N.14. Soil.NO3N.15. Soil.NO3N.16.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.068
1st Qu.: 0.531 1st Qu.: 0.466 1st Qu.: 0.410 1st Qu.: 0.346 1st Qu.: 0.339 1st Qu.: 0.407
Median : 0.767 Median : 0.657 Median : 0.585 Median : 0.556 Median : 0.491 Median : 0.506
Mean : 1.506 Mean : 1.332 Mean : 1.250 Mean : 1.177 Mean : 1.114 Mean : 1.103
Soil.NO3N.17. Soil.NO3N.18. Soil.NO3N.19. Soil.NO3N.20. Soil.NO3N.21. Soil.NO3N.22.
Min. :0.133 Min. :0.098 Min. :0.076 Min. :0.098 Min. :0.098 Min. :0.095
1st Qu.:0.356 1st Qu.:0.325 1st Qu.:0.304 1st Qu.:0.287 1st Qu.:0.268 1st Qu.:0.261
Median :0.465 Median :0.424 Median :0.402 Median :0.389 Median :0.392 Median :0.469
Mean :0.863 Mean :0.795 Mean :0.754 Mean :0.730 Mean :0.727 Mean :0.746
Soil.NO3N.23. DiagnosticsVariables.Script.SoilNitrogenContent Lucerne.Arbitrator.N.TotalPlantDemand
Min. :0.092 Min. : 0.00 Min. :0.000000
1st Qu.:0.254 1st Qu.: 19.81 1st Qu.:0.004248
Median :0.520 Median : 26.10 Median :0.077676
Mean :0.763 Mean : 54.96 Mean :0.162730
DiagnosticsVariables.Script.FomN DiagnosticsVariables.Script.HumN DiagnosticsVariables.Script.BiomN
Min. : 0.0000 Min. : 0 Min. : 0.0
1st Qu.: 0.3819 1st Qu.:22701 1st Qu.: 99.5
Median : 1.1118 Median :22799 Median :128.2
Mean : 3.4498 Mean :20429 Mean :128.8
DiagnosticsVariables.Script.DltNMinRes DiagnosticsVariables.Script.DltNMinTot Lucerne.Leaf.Fw
Min. :-2.898e-01 Min. :-0.02068 Min. :0.0000
1st Qu.: 0.000e+00 1st Qu.: 0.08718 1st Qu.:1.0000
Median : 0.000e+00 Median : 0.16660 Median :1.0000
Mean :-3.008e-05 Mean : 0.19528 Mean :0.9052
Lucerne.Leaf.Fn Lucerne.Phenology.CurrentPhaseName Lucerne.Phenology.CurrentStageName
Min. :0.0000000 Length:110826 Length:110826
1st Qu.:0.0000000 Class :character Class :character
Median :0.0000000 Mode :character Mode :character
Mean :0.0690763
Lucerne.Phenology.Stage Lucerne.Phenology.DaysAfterCutting.Value..
Min. :1.000 Min. : 0.00
1st Qu.:4.433 1st Qu.: 17.00
Median :5.082 Median : 37.00
Mean :5.563 Mean : 34.82
Lucerne.Phenology.FloweringDaysAfterCutting.Value.. Lucerne.Pod.Wt Lucerne.Pod.N shootbiomass
Min. : 0.00 Min. : 0.00000 Min. :0.000000 Min. : 0.0
1st Qu.: 37.00 1st Qu.: 0.00000 1st Qu.:0.000000 1st Qu.: 556.5
Median : 46.00 Median : 0.00000 Median :0.000000 Median : 2027.8
Mean : 44.78 Mean : 10.64277 Mean :0.319283 Mean : 7803.4
Lucerne.Leaf.LAIFunction.Value.. Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2.
Min. : 0.000 Min. :0.00000 Min. :0.000000
1st Qu.: 1.215 1st Qu.:0.02057 1st Qu.:0.004291
Median : 3.344 Median :0.03875 Median :0.012039
Mean : 7.504 Mean :0.03834 Mean :0.019694
Lucerne.Root.LengthDensity.3. Lucerne.Root.LengthDensity.4. Lucerne.Root.LengthDensity.5.
Min. :0.000000 Min. :0.000000 Min. :0.000000
1st Qu.:0.002882 1st Qu.:0.002627 1st Qu.:0.002886
Median :0.008293 Median :0.007560 Median :0.007810
Mean :0.012066 Mean :0.009343 Mean :0.009403
Lucerne.Root.LengthDensity.6. Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8.
Min. :0.0000 Min. :0.0000 Min. :0.000
1st Qu.:0.0030 1st Qu.:0.0029 1st Qu.:0.003
Median :0.0087 Median :0.0086 Median :0.009
Mean :0.0100 Mean :0.0097 Mean :0.009
Lucerne.Root.LengthDensity.9. Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.003 1st Qu.:0.003 1st Qu.:0.003
Median :0.008 Median :0.008 Median :0.007
Mean :0.008 Mean :0.008 Mean :0.008
Lucerne.Root.LengthDensity.12. Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.003 1st Qu.:0.002 1st Qu.:0.002
Median :0.006 Median :0.006 Median :0.006
Mean :0.007 Mean :0.007 Mean :0.007
Lucerne.Root.LengthDensity.15. Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.002 1st Qu.:0.002 1st Qu.:0.002
Median :0.005 Median :0.005 Median :0.003
Mean :0.006 Mean :0.005 Mean :0.004
Lucerne.Root.LengthDensity.18. Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.001 1st Qu.:0.001
Median :0.003 Median :0.003 Median :0.003
Mean :0.004 Mean :0.003 Mean :0.004
Lucerne.Root.LengthDensity.21. Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.001 1st Qu.:0.001 1st Qu.:0.001
Median :0.003 Median :0.005 Median :0.006
Mean :0.003 Mean :0.006 Mean :0.007
Soil.SoilWater.WaterTable Lucerne.AboveGround.Wt Lucerne.AboveGround.N Soil.SoilWater.ESW.1.
Min. : 0.784 Min. : 0.00 Min. : 0.0000 Min. : 0.00
1st Qu.: 600.000 1st Qu.: 55.65 1st Qu.: 0.5872 1st Qu.:13.31
Median :2300.000 Median : 202.78 Median : 2.8688 Median :20.96
Mean :1753.611 Mean : 780.34 Mean : 18.5285 Mean :18.56
Soil.SoilWater.ESW.2. Soil.SoilWater.ESW.3. Soil.SoilWater.ESW.4. Soil.SoilWater.ESW.5. Soil.SoilWater.ESW.6.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:14.61 1st Qu.:14.45 1st Qu.:12.20 1st Qu.: 11.16 1st Qu.:11.70
Median :22.85 Median :18.13 Median :18.03 Median : 19.91 Median :20.59
Mean :19.47 Mean :17.59 Mean :16.44 Mean : 16.28 Mean :17.13
Soil.SoilWater.ESW.7. Soil.SoilWater.ESW.8. Soil.SoilWater.ESW.9. Soil.SoilWater.ESW.10.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 9.127 1st Qu.: 6.943 1st Qu.: 7.811 1st Qu.: 9.083
Median :17.913 Median :19.681 Median :19.556 Median :19.988
Mean :15.805 Mean :16.513 Mean :16.223 Mean :17.092
Soil.SoilWater.ESW.11. Soil.SoilWater.ESW.12. Soil.SoilWater.ESW.13. Soil.SoilWater.ESW.14.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:10.73 1st Qu.:13.54 1st Qu.:12.99 1st Qu.:13.10
Median :20.70 Median :22.13 Median :20.00 Median :21.31
Mean :17.86 Mean :19.26 Mean :17.91 Mean :18.38
Soil.SoilWater.ESW.15. Soil.SoilWater.ESW.16. Soil.SoilWater.ESW.17. Soil.SoilWater.ESW.18.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:13.55 1st Qu.:13.35 1st Qu.:16.02 1st Qu.:16.02
Median :23.41 Median :21.74 Median :21.73 Median :22.66
Mean :19.19 Mean :19.11 Mean :19.68 Mean :20.02
Soil.SoilWater.ESW.19. Soil.SoilWater.ESW.20. Soil.SoilWater.ESW.21. Soil.SoilWater.ESW.22.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.:16.02 1st Qu.:17.03 1st Qu.:14.88 1st Qu.:14.01
Median :23.44 Median :24.24 Median :23.17 Median :22.96
Mean :20.30 Mean :21.21 Mean :20.30 Mean :19.90
Soil.SoilWater.ESW.23. Water SowingDate FD SD1
Min. : 6.00 Length:110826 Length:110826 Length:110826 Length:110826
1st Qu.:16.10 Class :character Class :character Class :character Class :character
Median :23.09 Mode :character Mode :character Mode :character Mode :character
Mean :21.89
LL SS Cutting Factors Soil.OutputLayers.SWmm.1.
Length:110826 Length:110826 Length:110826 Length:110826 Min. : 3.00
Class :character Class :character Class :character Class :character 1st Qu.: 15.36
Mode :character Mode :character Mode :character Mode :character Median : 22.76
Mean : 25.89
Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3. Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5.
Min. : 21.00 Min. : 42.09 Min. : 0.00 Min. : 51.33
1st Qu.: 28.25 1st Qu.: 54.79 1st Qu.: 50.99 1st Qu.: 86.04
Median : 61.62 Median : 71.16 Median : 80.00 Median :118.70
Mean : 74.09 Mean : 86.21 Mean : 85.57 Mean :113.74
Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7. Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1.
Min. : 54.78 Min. : 57.97 Min. : 60.88 Min. :0.03
1st Qu.: 74.01 1st Qu.: 90.00 1st Qu.: 88.00 1st Qu.:0.08
Median : 84.47 Median : 90.73 Median : 90.19 Median :0.18
Mean :102.25 Mean :107.72 Mean :136.96 Mean :0.20
Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3. Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5.
Min. :0.05 Min. :0.05 Min. :0.00 Min. :0.22
1st Qu.:0.09 1st Qu.:0.16 1st Qu.:0.20 1st Qu.:0.23
Median :0.22 Median :0.23 Median :0.25 Median :0.29
Mean :0.23 Mean :0.24 Mean :0.25 Mean :0.29
Soil.OutputLayers.SW.6. Soil.OutputLayers.SW.7. Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9.
Min. :0.19 Min. :0.22 Min. :0.22 Min. :63.00
1st Qu.:0.19 1st Qu.:0.22 1st Qu.:0.22 1st Qu.:63.34
Median :0.28 Median :0.29 Median :0.31 Median :63.70
Mean :0.28 Mean :0.30 Mean :0.30 Mean :63.66
Soil.OutputLayers.SWmm.10. Soil.OutputLayers.SWmm.11. Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13.
Min. :64.01 Min. :64.00 Min. :60.10 Min. :58.00
1st Qu.:64.33 1st Qu.:64.00 1st Qu.:60.15 1st Qu.:58.00
Median :64.77 Median :64.00 Median :60.29 Median :58.00
Mean :64.87 Mean :64.35 Mean :60.69 Mean :58.00
Soil.OutputLayers.SWmm.14. Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17.
Min. :54.00 Min. :54.00 Min. :54.00 Min. :54.50
1st Qu.:54.01 1st Qu.:54.01 1st Qu.:54.34 1st Qu.:54.57
Median :54.20 Median :54.06 Median :54.43 Median :54.67
Mean :54.23 Mean :54.05 Mean :54.40 Mean :55.06
Soil.OutputLayers.SWmm.18. Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20. Soil.OutputLayers.SWmm.21.
Min. :58.01 Min. :60.84 Min. :61.20 Min. :58.82
1st Qu.:58.81 1st Qu.:61.69 1st Qu.:62.48 1st Qu.:60.49
Median :59.11 Median :62.50 Median :63.82 Median :63.13
Mean :59.03 Mean :62.46 Mean :63.96 Mean :63.65
Soil.OutputLayers.SWmm.22. Soil.OutputLayers.SWmm.23. Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10.
Min. :51.15 Min. :0 Min. :0.32 Min. :0.32
1st Qu.:51.55 1st Qu.:0 1st Qu.:0.32 1st Qu.:0.32
Median :53.14 Median :0 Median :0.32 Median :0.32
Mean :56.05 Mean :0 Mean :0.32 Mean :0.32
Soil.OutputLayers.SW.11. Soil.OutputLayers.SW.12. Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14.
Min. :0.32 Min. :0.30 Min. :0.29 Min. :0.27
1st Qu.:0.32 1st Qu.:0.30 1st Qu.:0.29 1st Qu.:0.27
Median :0.32 Median :0.30 Median :0.29 Median :0.27
Mean :0.32 Mean :0.30 Mean :0.29 Mean :0.27
Soil.OutputLayers.SW.15. Soil.OutputLayers.SW.16. Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18.
Min. :0.27 Min. :0.27 Min. :0.27 Min. :0.29
1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.29
Median :0.27 Median :0.27 Median :0.27 Median :0.30
Mean :0.27 Mean :0.27 Mean :0.28 Mean :0.30
Soil.OutputLayers.SW.19. Soil.OutputLayers.SW.20. Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22.
Min. :0.30 Min. :0.31 Min. :0.29 Min. :0.26
1st Qu.:0.31 1st Qu.:0.31 1st Qu.:0.30 1st Qu.:0.26
Median :0.31 Median :0.32 Median :0.32 Median :0.27
Mean :0.31 Mean :0.32 Mean :0.32 Mean :0.28
Soil.OutputLayers.SW.23. SWmm.24. SWmm.25. SWmm.26. SWmm.27. Soil.NO3N.24.
Min. :0 Min. :58.00 Min. :58.00 Min. :58.00 Min. :58.00 Min. :0.05
1st Qu.:0 1st Qu.:58.63 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:0.22
Median :0 Median :60.00 Median :60.00 Median :60.00 Median :60.00 Median :0.28
Mean :0 Mean :59.37 Mean :59.76 Mean :59.75 Mean :59.75 Mean :1.11
Soil.NO3N.25. Soil.NO3N.26. Soil.NO3N.27. Lucerne.Root.LengthDensity.24.
Min. :0.00 Min. :0.00 Min. :0.00 Min. :0
1st Qu.:0.02 1st Qu.:0.02 1st Qu.:0.02 1st Qu.:0
Median :0.19 Median :0.19 Median :0.19 Median :0
Mean :1.03 Mean :1.03 Mean :1.03 Mean :0
Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26. Lucerne.Root.LengthDensity.27.
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
Soil.SoilWater.ESW.24. Soil.SoilWater.ESW.25. Soil.SoilWater.ESW.26. Soil.SoilWater.ESW.27. Name
Min. :14.00 Min. :14.00 Min. :14.00 Min. :14.00 Length:110826
1st Qu.:14.63 1st Qu.:16.00 1st Qu.:16.00 1st Qu.:16.00 Class :character
Median :16.00 Median :16.00 Median :16.00 Median :16.00 Mode :character
Mean :15.37 Mean :15.76 Mean :15.75 Mean :15.75
[ reached getOption("max.print") -- omitted 3 rows ]# myDb %>%
# dplyr::select(Name) %>%
# unique()Prepare merge
Add info for merging
select variables that are for comparing with observed data
simD <- myDb %>%
dplyr::select(Name,Clock.Today,LAI,SWC,Height,shootbiomass,RootWt, StemWt, LeafWt,NodeNumber) %>%
tidyr::gather("Variable","Predicted",LAI:NodeNumber) %>%
mutate(Name = as.factor(Name)) %>%
mutate(Variable = as.factor(Variable)) %>%
mutate(Clock.Today = ymd_hms(Clock.Today))
head(simD)summary(simD) Name Clock.Today Variable Predicted
RutherglenDefoliation : 20448 Min. :1979-01-01 12:00:00 Height :110826 Min. : 0.0
HudsonDefoliation : 15984 1st Qu.:1999-08-11 12:00:00 LAI :110826 1st Qu.: 20.9
Iversen_8FWaterdryCuttingGR22: 15152 Median :2001-08-28 12:00:00 LeafWt :110826 Median : 547.4
Iversen_8FWaterdryCuttingGR23: 15152 Mean :2003-01-29 03:05:51 NodeNumber :110826 Mean : 2736.2
Iversen_8FWaterdryCuttingGR25: 15152 3rd Qu.:2004-02-12 12:00:00 RootWt :110826 3rd Qu.: 2395.3
Iversen_8FWaterdryCuttingGR32: 15152 Max. :2018-01-01 12:00:00 shootbiomass:110826 Max. :48775.7
(Other) :789568 (Other) :221652 head(obsNode)mergedf<-merge(obsA,simD,by=c("Clock.Today","Name","Variable"))
summary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :791 1997_2001: 916
1st Qu.:2001-01-06 Iversen_8Waterdry :461 Height :696 2000_2002:1020
Median :2001-09-15 Iversen_8Waterirr :455 shootbiomass:585 2002_2004: 586
Mean :2004-07-16 Iversen_121DefoliationLLFDFD5 :415 LAI :537 2010_2012: 0
3rd Qu.:2004-03-02 Iversen_9SowingDateSD1Waterirr:263 SWC :282 2014_2018: 676
Max. :2017-12-29 Iversen_9SowingDateSD1Waterdry:258 RootWt :110
(Other) :760 (Other) :197
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:1140 dry: 972 HH: 192 No :1502 FD10: 35 00_01 :963 Min. :1.000
Lincoln2015: 674 irr:2226 LL:3006 no : 676 FD2 : 34 01_02 :468 1st Qu.:1.000
Lincoln2001: 379 SD1 : 521 FD5 :3129 02_03 :274 Median :2.000
Lincoln2003: 286 SD2 : 186 03_04 :273 Mean :2.336
Lincoln2004: 178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1998: 168 SD4 : 155 16_17 :201 Max. :6.000
(Other) : 373 (Other): 0 (Other):777
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-11-14 1st Qu.:2000-12-29 1st Qu.:2001-01-24 1st Qu.: 20.00
Median :2.000 Median :2001-07-05 Median :2001-08-16 Median :2001-09-29 Median : 33.00
Mean :2.952 Mean :2004-06-08 Mean :2004-07-09 Mean :2004-08-10 Mean : 37.88
3rd Qu.:4.000 3rd Qu.:2004-02-08 3rd Qu.:2004-02-28 3rd Qu.:2004-03-19 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:3198 Min. : 0.00 Min. : 0.0000 E1: 916 ILL :1041
cm :453 1st Qu.: 5.35 1st Qu.: 0.0000 E2:1020 DLL : 461
fractio0l:575 Median : 20.03 Median : 0.3462 E3: 586 ILLF5 : 415
Fraction :216 Mean : 661.95 Mean : 94.5968 E4: 0 ILLS1 : 263
kg/ha :892 3rd Qu.: 649.67 3rd Qu.: 35.7600 E5: 676 DLLS1 : 258
m2/m2 :537 Max. :8556.38 Max. :2532.5264 IHHF5 : 192
mm :525 NA's :648 (Other): 568
ID GrowthSeason1 Rotation1 ExpUnitCode Clock.Today1
E3ILL :586 Gs_1:1044 Rt_1:967 Iversen_91DefoliationLLGs_2Rt_1: 59 Min. :1997-01-28 12:00:00
E1DLL :461 Gs_2:1127 Rt_2:646 Iversen_91DefoliationLLGs_1Rt_1: 48 1st Qu.:2001-01-06 12:00:00
E1ILL :455 Gs_3: 388 Rt_3:457 Iversen_8WaterdryGs_5Rt_1 : 42 Median :2001-09-15 12:00:00
E5ILLF5:415 Gs_4: 228 Rt_4:354 Iversen_8WaterirrGs_5Rt_1 : 42 Mean :2004-07-17 03:12:42
E2ILLS1:263 Gs_5: 369 Rt_5:354 Iversen_91DefoliationLLGs_1Rt_4: 42 3rd Qu.:2004-03-02 12:00:00
E2DLLS1:258 Gs_6: 42 Rt_6:317 Iversen_91DefoliationLLGs_1Rt_5: 42 Max. :2017-12-29 12:00:00
(Other):760 Rt_7:103 (Other) :2923
GrowthSeason2 Rotation2 Predicted
Y1(00_01): 594 R2 :646 Min. : 0.000
Y2(01_02): 426 S1 :634 1st Qu.: 5.548
Y5(00_01): 369 R3 :457 Median : 133.087
Y1(02_03): 274 R4 :354 Mean : 773.700
Y2(03_04): 273 R5 :354 3rd Qu.: 690.120
Y2(15_16): 242 R1 :333 Max. :7999.825
(Other) :1020 (Other):420 str(mergedf)'data.frame': 3198 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 69.05 69.05 4.82 4.82 2939.64 ...mergedfNode number
Time series
obs Vs Pre for each experiment
1997-2001
obsNode1<-obsA%>%
dplyr::filter(Collection=="1997_2001")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_8Waterirr")%>%
dplyr::filter(Clock.Today>"1997-08-01")%>%
dplyr::filter(Clock.Today<"2001-08-01")
simD1<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"1997-08-01")%>%
dplyr::filter(Clock.Today<"2001-08-01")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_8Waterirr")
str(simD1)'data.frame': 1460 obs. of 4 variables:
$ Name : Factor w/ 103 levels "AshleyDeneSowingDateSD1",..: 49 49 49 49 49 49 49 49 49 49 ...
$ Clock.Today: POSIXct, format: "1998-08-03 12:00:00" "1998-05-11 12:00:00" "1997-09-03 12:00:00" "1997-08-26 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Predicted : num 3.14 12.13 7.55 6.54 6.08 ... simD1%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~ID,ncol = 2)+
geom_point(data=obsNode1, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))+
annotate("text", x=ymd_hms("1999-10-24 12:00:00"), y=17, size = 5, label ="paste(R_RMSD == 21.8,'%')", parse=T)
#ggsave("D:/R/Pictures/Node/Iversen_8Waterirrnodenumber.png", width=8, height=4, dpi=500)2002-2004
obsNode2<-obsNode%>%
dplyr::filter(Collection=="2002_2004")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
simD2<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"2002-06-01")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
str(simD2)'data.frame': 884 obs. of 4 variables:
$ Name : Factor w/ 103 levels "AshleyDeneSowingDateSD1",..: 50 50 50 50 50 50 50 50 50 50 ...
$ Clock.Today: POSIXct, format: "2004-09-22 12:00:00" "2003-03-21 12:00:00" "2003-03-22 12:00:00" "2003-03-23 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Predicted : num 9.205 11.556 11.835 0.274 0.738 ...simD2%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
#facet_wrap(~ID,ncol = 1)+
geom_point(data=obsNode2, aes(x=Clock.Today1, y=Observed),colour="blue",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))+
annotate("text", x=ymd_hms("2003-07-24 12:00:00"), y=17, size = 5, label ="paste(R_RMSD == 21.9,'%')", parse=T)
#ggsave("D:/R/Pictures/Node/Iversen_91DefoliationLLnodenumber.png", width=8, height=4, dpi=500)
ggsave("D:/R/Pictures/Poster/E3.png", width=8, height=4, dpi=500)
2000-2002
obsNode3<-obsNode%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Variable=="NodeNumber")
obsNode3 simD3a<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"2000-10-24 12:00:00")%>%
dplyr::filter(Clock.Today<"2002-07-01 12:00:00")%>%
dplyr::filter(Name==c("Iversen_9SowingDateSD1Waterirr","Iversen_9SowingDateSD2Waterirr","Iversen_9SowingDateSD3Waterirr","Iversen_9SowingDateSD4Waterirr"))%>%
dplyr::filter(Variable=="NodeNumber")
DF<-data.frame(Name=c("Iversen_9SowingDateSD1Waterirr","Iversen_9SowingDateSD2Waterirr","Iversen_9SowingDateSD3Waterirr","Iversen_9SowingDateSD4Waterirr"),ID= c("E2ILLS1","E2ILLS2","E2ILLS3","E2ILLS4"))
simD3<-merge(DF,simD3a, by=c("Name"))
G<-simD3%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
geom_point(data=obsNode3, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
facet_wrap(~ID,ncol = 2)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))
dat_text1 <- data.frame(
label = c("R_RMSE=27.7%", "R_RMSE=28%", "R_RMSE=21.8%","R_RMSE=24.5%"),
ID= c("E2ILLS1","E2ILLS2","E2ILLS3","E2ILLS4"),
x= ymd_hms("2001-03-24 12:00:00", "2001-03-24 12:00:00","2001-03-24 12:00:00","2001-03-24 12:00:00"),
y=c(17,17,17,17))
G+geom_text(data=dat_text1, mapping = aes(x=x,y=y, label = label),hjust = -0.1,vjust = -1,size=5)
#ggsave("D:/R/Pictures/Node/Iversen_9SowingDatenodenumber.png", width=8, height=8, dpi=500)
2014-2018
obsNode4<-obsNode%>%
dplyr::filter(Collection=="2014_2018")%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")
simD4a<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
dplyr::filter(Clock.Today<"2015-01-06")
simD4b<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="NodeNumber")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
dplyr::filter(Clock.Today>"2015-01-30")
simD4<-rbind(simD4a,simD4b)
str(simD4)'data.frame': 1158 obs. of 4 variables:
$ Name : Factor w/ 103 levels "AshleyDeneSowingDateSD1",..: 20 20 20 20 20 20 20 20 20 20 ...
$ Clock.Today: POSIXct, format: "2014-10-08 12:00:00" "2014-10-09 12:00:00" "2014-10-10 12:00:00" "2014-10-11 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 4 4 4 4 4 4 4 4 4 4 ...
$ Predicted : num 0 0 0 0 0 0 0 0 0 0 ...simD4%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~ID,ncol = 1)+
geom_point(data=obsNode4, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Node number")+
#remove grid lines
theme(
panel.spacing=unit(.01, "lines"),
panel.border = element_rect(colour = "black",size=1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black",size = 1),
axis.text.x = element_text(face = "plain", color = "black", size = 14, vjust=0.5, hjust = 1),
axis.text.y = element_text(face = "plain", color = "black", size = 14),
axis.title.x=element_text(face="bold",colour="black",size = 14),
axis.title.y=element_text(face="bold",colour="black",size = 14),
strip.background = element_rect(colour = "black", fill = "white",size=1),
strip.text.x = element_text(size=14, angle=0, face = "plain"),
strip.text.y = element_text(size=14, face="plain"),
legend.title = element_text(colour="black", size=14, face="bold"),
axis.text = element_text(face = "bold", vjust = 0.5, size = 14))+
annotate("text", x=ymd_hms("2016-07-24 12:00:00"), y=18, size = 5, label ="paste(R_RMSD == 30,'%')", parse=T)
#ggsave("D:/R/Pictures/Node/Iversen_121DefoliationLLFDFD5nodenumber.png", width=8, height=4, dpi=500)
Statistic and Graph
2002-2004
mergedfsummary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :791 1997_2001: 916
1st Qu.:2001-01-06 Iversen_8Waterdry :461 Height :696 2000_2002:1020
Median :2001-09-15 Iversen_8Waterirr :455 shootbiomass:585 2002_2004: 586
Mean :2004-07-16 Iversen_121DefoliationLLFDFD5 :415 LAI :537 2010_2012: 0
3rd Qu.:2004-03-02 Iversen_9SowingDateSD1Waterirr:263 SWC :282 2014_2018: 676
Max. :2017-12-29 Iversen_9SowingDateSD1Waterdry:258 RootWt :110
(Other) :760 (Other) :197
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:1140 dry: 972 HH: 192 No :1502 FD10: 35 00_01 :963 Min. :1.000
Lincoln2015: 674 irr:2226 LL:3006 no : 676 FD2 : 34 01_02 :468 1st Qu.:1.000
Lincoln2001: 379 SD1 : 521 FD5 :3129 02_03 :274 Median :2.000
Lincoln2003: 286 SD2 : 186 03_04 :273 Mean :2.336
Lincoln2004: 178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1998: 168 SD4 : 155 16_17 :201 Max. :6.000
(Other) : 373 (Other): 0 (Other):777
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-11-14 1st Qu.:2000-12-29 1st Qu.:2001-01-24 1st Qu.: 20.00
Median :2.000 Median :2001-07-05 Median :2001-08-16 Median :2001-09-29 Median : 33.00
Mean :2.952 Mean :2004-06-08 Mean :2004-07-09 Mean :2004-08-10 Mean : 37.88
3rd Qu.:4.000 3rd Qu.:2004-02-08 3rd Qu.:2004-02-28 3rd Qu.:2004-03-19 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:3198 Min. : 0.00 Min. : 0.0000 E1: 916 ILL :1041
cm :453 1st Qu.: 5.35 1st Qu.: 0.0000 E2:1020 DLL : 461
fractio0l:575 Median : 20.03 Median : 0.3462 E3: 586 ILLF5 : 415
Fraction :216 Mean : 661.95 Mean : 94.5968 E4: 0 ILLS1 : 263
kg/ha :892 3rd Qu.: 649.67 3rd Qu.: 35.7600 E5: 676 DLLS1 : 258
m2/m2 :537 Max. :8556.38 Max. :2532.5264 IHHF5 : 192
mm :525 NA's :648 (Other): 568
ID GrowthSeason1 Rotation1 ExpUnitCode Clock.Today1
E3ILL :586 Gs_1:1044 Rt_1:967 Iversen_91DefoliationLLGs_2Rt_1: 59 Min. :1997-01-28 12:00:00
E1DLL :461 Gs_2:1127 Rt_2:646 Iversen_91DefoliationLLGs_1Rt_1: 48 1st Qu.:2001-01-06 12:00:00
E1ILL :455 Gs_3: 388 Rt_3:457 Iversen_8WaterdryGs_5Rt_1 : 42 Median :2001-09-15 12:00:00
E5ILLF5:415 Gs_4: 228 Rt_4:354 Iversen_8WaterirrGs_5Rt_1 : 42 Mean :2004-07-17 03:12:42
E2ILLS1:263 Gs_5: 369 Rt_5:354 Iversen_91DefoliationLLGs_1Rt_4: 42 3rd Qu.:2004-03-02 12:00:00
E2DLLS1:258 Gs_6: 42 Rt_6:317 Iversen_91DefoliationLLGs_1Rt_5: 42 Max. :2017-12-29 12:00:00
(Other):760 Rt_7:103 (Other) :2923
GrowthSeason2 Rotation2 Predicted
Y1(00_01): 594 R2 :646 Min. : 0.000
Y2(01_02): 426 S1 :634 1st Qu.: 5.548
Y5(00_01): 369 R3 :457 Median : 133.087
Y1(02_03): 274 R4 :354 Mean : 773.700
Y2(03_04): 273 R5 :354 3rd Qu.: 690.120
Y2(15_16): 242 R1 :333 Max. :7999.825
(Other) :1020 (Other):420 str(mergedf)'data.frame': 3198 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 69.05 69.05 4.82 4.82 2939.64 ...mergedf %>%
dplyr::filter(Collection=="2002_2004")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="2014_2018")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
2000-2002
mergedfsummary(mergedf) Clock.Today Name Variable Collection
Min. :1997-01-28 Iversen_91DefoliationLL :586 NodeNumber :791 1997_2001: 916
1st Qu.:2001-01-06 Iversen_8Waterdry :461 Height :696 2000_2002:1020
Median :2001-09-15 Iversen_8Waterirr :455 shootbiomass:585 2002_2004: 586
Mean :2004-07-16 Iversen_121DefoliationLLFDFD5 :415 LAI :537 2010_2012: 0
3rd Qu.:2004-03-02 Iversen_9SowingDateSD1Waterirr:263 SWC :282 2014_2018: 676
Max. :2017-12-29 Iversen_9SowingDateSD1Waterdry:258 RootWt :110
(Other) :760 (Other) :197
Experiment Water Defoliation SowingDate FD Period GrowthSeason
Lincoln2000:1140 dry: 972 HH: 192 No :1502 FD10: 35 00_01 :963 Min. :1.000
Lincoln2015: 674 irr:2226 LL:3006 no : 676 FD2 : 34 01_02 :468 1st Qu.:1.000
Lincoln2001: 379 SD1 : 521 FD5 :3129 02_03 :274 Median :2.000
Lincoln2003: 286 SD2 : 186 03_04 :273 Mean :2.336
Lincoln2004: 178 SD3 : 158 15_16 :242 3rd Qu.:3.000
Lincoln1998: 168 SD4 : 155 16_17 :201 Max. :6.000
(Other) : 373 (Other): 0 (Other):777
Rotation StartDate MidDate FinishDate Interval
Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21 Min. : 0.00
1st Qu.:1.000 1st Qu.:2000-11-14 1st Qu.:2000-12-29 1st Qu.:2001-01-24 1st Qu.: 20.00
Median :2.000 Median :2001-07-05 Median :2001-08-16 Median :2001-09-29 Median : 33.00
Mean :2.952 Mean :2004-06-08 Mean :2004-07-09 Mean :2004-08-10 Mean : 37.88
3rd Qu.:4.000 3rd Qu.:2004-02-08 3rd Qu.:2004-02-28 3rd Qu.:2004-03-19 3rd Qu.: 52.00
Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :122.00
VariableUnits Time Observed StdDEV ExperimentID TreatmentID
% : 0 12:00:00:3198 Min. : 0.00 Min. : 0.0000 E1: 916 ILL :1041
cm :453 1st Qu.: 5.35 1st Qu.: 0.0000 E2:1020 DLL : 461
fractio0l:575 Median : 20.03 Median : 0.3462 E3: 586 ILLF5 : 415
Fraction :216 Mean : 661.95 Mean : 94.5968 E4: 0 ILLS1 : 263
kg/ha :892 3rd Qu.: 649.67 3rd Qu.: 35.7600 E5: 676 DLLS1 : 258
m2/m2 :537 Max. :8556.38 Max. :2532.5264 IHHF5 : 192
mm :525 NA's :648 (Other): 568
ID GrowthSeason1 Rotation1 ExpUnitCode Clock.Today1
E3ILL :586 Gs_1:1044 Rt_1:967 Iversen_91DefoliationLLGs_2Rt_1: 59 Min. :1997-01-28 12:00:00
E1DLL :461 Gs_2:1127 Rt_2:646 Iversen_91DefoliationLLGs_1Rt_1: 48 1st Qu.:2001-01-06 12:00:00
E1ILL :455 Gs_3: 388 Rt_3:457 Iversen_8WaterdryGs_5Rt_1 : 42 Median :2001-09-15 12:00:00
E5ILLF5:415 Gs_4: 228 Rt_4:354 Iversen_8WaterirrGs_5Rt_1 : 42 Mean :2004-07-17 03:12:42
E2ILLS1:263 Gs_5: 369 Rt_5:354 Iversen_91DefoliationLLGs_1Rt_4: 42 3rd Qu.:2004-03-02 12:00:00
E2DLLS1:258 Gs_6: 42 Rt_6:317 Iversen_91DefoliationLLGs_1Rt_5: 42 Max. :2017-12-29 12:00:00
(Other):760 Rt_7:103 (Other) :2923
GrowthSeason2 Rotation2 Predicted
Y1(00_01): 594 R2 :646 Min. : 0.000
Y2(01_02): 426 S1 :634 1st Qu.: 5.548
Y5(00_01): 369 R3 :457 Median : 133.087
Y1(02_03): 274 R4 :354 Mean : 773.700
Y2(03_04): 273 R5 :354 3rd Qu.: 690.120
Y2(15_16): 242 R1 :333 Max. :7999.825
(Other) :1020 (Other):420 str(mergedf)'data.frame': 3198 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 69.05 69.05 4.82 4.82 2939.64 ...mergedf %>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Node Number")+
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank())+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="1997_2001")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw() +
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1) +
ggtitle("Node Number") +
facet_grid(GrowthSeason~Rotation)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="1997_2001")%>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw() +
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1) +
ggtitle("Node Number") +
# facet_grid(.~Rotation) + # Rotation
facet_wrap(~GrowthSeason, ncol = 4)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
RMSE
str(mergedf)'data.frame': 3198 obs. of 30 variables:
$ Clock.Today : Date, format: "1997-01-28" "1997-01-28" "1997-02-17" "1997-02-17" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 15 16 15 16 15 16 15 16 15 16 ...
$ Variable : Factor w/ 19 levels "Branch","CrownWt",..: 13 13 6 6 13 13 13 13 13 13 ...
$ Collection : Factor w/ 5 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water : Factor w/ 2 levels "dry","irr": 1 2 1 2 1 2 1 2 1 2 ...
$ Defoliation : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ Period : Factor w/ 16 levels "00_01","01_02",..: 13 13 13 13 13 13 13 13 14 14 ...
$ GrowthSeason : int 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation : int 1 1 1 1 1 1 2 2 1 1 ...
$ StartDate : Date, format: "1996-11-01" "1996-11-01" "1996-11-01" "1996-11-01" ...
$ MidDate : Date, format: "1996-12-27" "1996-12-27" "1996-12-27" "1996-12-27" ...
$ FinishDate : Date, format: "1997-02-21" "1997-02-21" "1997-02-21" "1997-02-21" ...
$ Interval : int 88 88 108 108 112 112 103 103 51 51 ...
$ VariableUnits: Factor w/ 7 levels "%","cm","fractio0l",..: 5 5 6 6 5 5 5 5 5 5 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 4613.3 4613.3 4.47 4.32 5400 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ ExperimentID : Factor w/ 5 levels "E1","E2","E3",..: 1 1 1 1 1 1 1 1 1 1 ...
$ TreatmentID : Factor w/ 20 levels "DLL","DLLS1",..: 1 13 1 13 1 13 1 13 1 13 ...
$ ID : Factor w/ 25 levels "E1DLL","E1ILL",..: 1 2 1 2 1 2 1 2 1 2 ...
$ GrowthSeason1: Factor w/ 6 levels "Gs_1","Gs_2",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 1 1 1 1 1 1 2 2 1 1 ...
$ ExpUnitCode : Factor w/ 199 levels "Iversen_8WaterdryGs_1Rt_1",..: 1 31 1 31 1 31 2 32 3 33 ...
$ Clock.Today1 : POSIXct, format: "1997-01-28 12:00:00" "1997-01-28 12:00:00" "1997-02-17 12:00:00" "1997-02-17 12:00:00" ...
$ GrowthSeason2: Factor w/ 18 levels "Y1(96_97)","Y2(97_98)",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Rotation2 : Factor w/ 8 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 1 1 ...
$ Predicted : num 69.05 69.05 4.82 4.82 2939.64 ...mergedf %>%
dplyr::filter(Variable== "NodeNumber") %>%
dplyr::filter(Water== "irr")%>%
dplyr::filter(Defoliation== "LL")%>%
#filter(Variable == "NodeNumber") %>%
filter(Collection!="2010_2012")%>%
mutate(Rotation= as.factor(Rotation))%>%
mutate(GrowthSeason=as.factor(GrowthSeason))%>%
group_by(ID) %>%
summarise(
n = n(),
r2 = gauchStats(Predicted,Observed)[5],
# rmse = round(rmse(Predicted,Observed),0),
r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
nse = round(NSE(Predicted,Observed),2),
sb = gauchStats(Predicted,Observed)[1],
nu = gauchStats(Predicted,Observed)[2],
lc = gauchStats(Predicted,Observed)[3]
) 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